{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:35:44Z","timestamp":1792794944267,"version":"4.5.0"},"posted":{"date-parts":[[2025,9,17]]},"group-title":"Developmental Biology","reference-count":74,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Foundation","doi-asserted-by":"crossref","award":["NNF21CC0073729"],"award-info":[{"award-number":["NNF21CC0073729"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Foundation","doi-asserted-by":"crossref","award":["NNF19SA0035442"],"award-info":[{"award-number":["NNF19SA0035442"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Foundation","doi-asserted-by":"crossref","award":["NNF22OC0076414"],"award-info":[{"award-number":["NNF22OC0076414"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Foundation","doi-asserted-by":"crossref","award":["NNF19OC0056962"],"award-info":[{"award-number":["NNF19OC0056962"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"crossref"}]},{"name":"European Research Council CoG","award":["101125803 MechanoFate"],"award-info":[{"award-number":["101125803 MechanoFate"]}],"id":[{"id":"https:\/\/ror.org\/0472cxd90","id-type":"ROR","asserted-by":"crossref"}]},{"award":["LF-OC-19-000219"],"award-info":[{"award-number":["LF-OC-19-000219"]}],"id":[{"id":"https:\/\/ror.org\/02rgsr590","id-type":"ROR","asserted-by":"publisher"}]},{"name":"GENCI-IDRIS","award":["A0161013396"],"award-info":[{"award-number":["A0161013396"]}]},{"name":"GENCI-IDRIS","award":["AD011013695"],"award-info":[{"award-number":["AD011013695"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,11,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Cell state transitions underlie the emergence of diverse cell types and are traditionally defined by changes in gene expression. Yet these transitions also involve coordinated shifts in cell morphology and behaviour, which remain poorly characterized in densely packed epithelia. We developed a quantitative live-imaging and computational framework to track thousands of individual cells over time in the rapidly differentiating Xenopus mucociliary epithelium (MCE). From segmentations and trajectories, we extracted dynamic features-cell and nuclear shape, movement, position-to create a time-resolved morphodynamic dataset spanning the full course of differentiation. While single features showed high noise and low separability of ground-truth cell types, supervised machine learning revealed that integrating time-resolved features robustly predicts final cell fate. Gradient-boosted trees and multinomial logistic regression achieved moderate but consistent accuracy, especially for abundant epithelial lineages. Key discriminants included normalized Z-position, membrane-nucleus offset, and absolute experimental time, whereas movement contributed minimally to the results. Our results show that morphodynamic signatures encode predictive information about cell identity and provide a framework linking physical dynamics with molecular state.<\/jats:p>","DOI":"10.1101\/2025.09.17.676780","type":"posted-content","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T21:30:26Z","timestamp":1758144626000},"source":"Crossref","is-referenced-by-count":1,"title":["Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4395-9022","authenticated-orcid":false,"given":"Mari","family":"Tolonen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1507-4358","authenticated-orcid":false,"given":"Ziwei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6226-7235","authenticated-orcid":false,"given":"Ozgur","family":"Beker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5331-7966","authenticated-orcid":false,"given":"Varun","family":"Kapoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8328-2354","authenticated-orcid":false,"given":"Bianca","family":"Dumitrascu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1788-0329","authenticated-orcid":false,"given":"Jakub","family":"Sedzinski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2025111010001683000_2025.09.17.676780v2.1","doi-asserted-by":"publisher","DOI":"10.1242\/dev.169748"},{"key":"2025111010001683000_2025.09.17.676780v2.2","doi-asserted-by":"publisher","DOI":"10.1002\/wsbm.1471"},{"key":"2025111010001683000_2025.09.17.676780v2.3","doi-asserted-by":"publisher","DOI":"10.1242\/dev.199950"},{"key":"2025111010001683000_2025.09.17.676780v2.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2024.04.035"},{"key":"2025111010001683000_2025.09.17.676780v2.5","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2018.00824"},{"key":"2025111010001683000_2025.09.17.676780v2.6","doi-asserted-by":"publisher","DOI":"10.1016\/j.devcel.2020.09.029"},{"key":"2025111010001683000_2025.09.17.676780v2.7","doi-asserted-by":"publisher","DOI":"10.1098\/rsob.210256"},{"key":"2025111010001683000_2025.09.17.676780v2.8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.2000640"},{"key":"2025111010001683000_2025.09.17.676780v2.9","doi-asserted-by":"crossref","first-page":"135a","DOI":"10.1016\/j.bpj.2021.11.2058","article-title":"Defining cell states and unraveling cell states transition through models of stochastic dynamics during differentiation","volume":"121","year":"2022","journal-title":"Biophys J"},{"key":"2025111010001683000_2025.09.17.676780v2.10","doi-asserted-by":"publisher","DOI":"10.15252\/msb.20178046"},{"key":"2025111010001683000_2025.09.17.676780v2.11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1146\/annurev-biodatasci-111419-091750","article-title":"Deciphering cell fate decision by integrated single-cell sequencing analysis","volume":"3","year":"2020","journal-title":"Annu Rev Biomed Data Sci"},{"key":"2025111010001683000_2025.09.17.676780v2.12","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.2859"},{"key":"2025111010001683000_2025.09.17.676780v2.13","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw3381"},{"key":"2025111010001683000_2025.09.17.676780v2.14","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-024-02303-9"},{"key":"2025111010001683000_2025.09.17.676780v2.15","doi-asserted-by":"publisher","DOI":"10.1016\/j.stem.2009.06.016"},{"key":"2025111010001683000_2025.09.17.676780v2.16","doi-asserted-by":"publisher","DOI":"10.1038\/nrm3620"},{"key":"2025111010001683000_2025.09.17.676780v2.17","doi-asserted-by":"publisher","DOI":"10.1152\/physiol.00026.2017"},{"key":"2025111010001683000_2025.09.17.676780v2.18","doi-asserted-by":"publisher","DOI":"10.1016\/j.yexcr.2019.03.016"},{"key":"2025111010001683000_2025.09.17.676780v2.19","doi-asserted-by":"publisher","DOI":"10.1016\/j.devcel.2021.05.011"},{"key":"2025111010001683000_2025.09.17.676780v2.20","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0909597107"},{"key":"2025111010001683000_2025.09.17.676780v2.21","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-58878-0"},{"key":"2025111010001683000_2025.09.17.676780v2.22","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1038\/s42256-022-00503-6","article-title":"Learning biophysical determinants of cell fate with deep neural networks","volume":"4","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2025111010001683000_2025.09.17.676780v2.23","doi-asserted-by":"publisher","DOI":"10.1038\/nature08869"},{"key":"2025111010001683000_2025.09.17.676780v2.24","doi-asserted-by":"publisher","DOI":"10.7554\/elife.24060"},{"key":"2025111010001683000_2025.09.17.676780v2.25","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-37447-3"},{"key":"2025111010001683000_2025.09.17.676780v2.26","doi-asserted-by":"crossref","first-page":"101229","DOI":"10.1016\/j.cels.2025.101229","article-title":"Geometric deep learning and multiple-instance learning for 3D cell-shape profiling","volume":"16","year":"2025","journal-title":"Cell Syst"},{"key":"2025111010001683000_2025.09.17.676780v2.27","doi-asserted-by":"crossref","first-page":"1228989","DOI":"10.3389\/fbinf.2023.1228989","article-title":"Machine learning enhanced cell tracking","volume":"3","year":"2023","journal-title":"Front Bioinform"},{"key":"2025111010001683000_2025.09.17.676780v2.28","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.59187"},{"key":"2025111010001683000_2025.09.17.676780v2.29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0240802"},{"key":"2025111010001683000_2025.09.17.676780v2.30","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.87949"},{"key":"2025111010001683000_2025.09.17.676780v2.31","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-13625-0"},{"key":"2025111010001683000_2025.09.17.676780v2.32","doi-asserted-by":"crossref","unstructured":"Bragantini J , Theodoro I , Zhao X , Huijben TAPM , Hirata-Miyasaki E , VijayKumar S , et al. Ultrack: pushing the limits of cell tracking across biological scales. Developmental Biology. bioRxiv; 2024. Available: https:\/\/www.biorxiv.org\/content\/10.1101\/2024.09.02.610652v1","DOI":"10.1101\/2024.09.02.610652"},{"key":"2025111010001683000_2025.09.17.676780v2.33","doi-asserted-by":"publisher","DOI":"10.1111\/j.1440-169X.1992.00051.x"},{"key":"2025111010001683000_2025.09.17.676780v2.34","doi-asserted-by":"publisher","DOI":"10.1242\/dev.126.21.4715"},{"key":"2025111010001683000_2025.09.17.676780v2.35","doi-asserted-by":"publisher","DOI":"10.1242\/dev.02417"},{"key":"2025111010001683000_2025.09.17.676780v2.36","doi-asserted-by":"publisher","DOI":"10.1242\/dev.055699"},{"key":"2025111010001683000_2025.09.17.676780v2.37","doi-asserted-by":"publisher","DOI":"10.1242\/dmm.006494"},{"key":"2025111010001683000_2025.09.17.676780v2.38","doi-asserted-by":"publisher","DOI":"10.1002\/dvdy.23706"},{"key":"2025111010001683000_2025.09.17.676780v2.39","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2019.08.063"},{"key":"2025111010001683000_2025.09.17.676780v2.40","doi-asserted-by":"crossref","first-page":"eadd5745","DOI":"10.1126\/sciadv.add5745","article-title":"A single-cell, time-resolved profiling of Xenopus mucociliary epithelium reveals nonhierarchical model of development","volume":"9","year":"2023","journal-title":"Sci Adv"},{"key":"2025111010001683000_2025.09.17.676780v2.41","first-page":"201","article-title":"The cellular basis of epiboly: an SEM study of deep-cell rearrangement during gastrulation in Xenopus laevis","volume":"60","year":"1980","journal-title":"J Embryol Exp Morphol"},{"key":"2025111010001683000_2025.09.17.676780v2.42","doi-asserted-by":"publisher","DOI":"10.1242\/dev.128.18.3635"},{"key":"2025111010001683000_2025.09.17.676780v2.43","doi-asserted-by":"publisher","DOI":"10.1016\/j.devcel.2016.04.008"},{"key":"2025111010001683000_2025.09.17.676780v2.44","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1010331108"},{"key":"2025111010001683000_2025.09.17.676780v2.45","doi-asserted-by":"publisher","DOI":"10.1109\/wacv45572.2020.9093435"},{"key":"2025111010001683000_2025.09.17.676780v2.46","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-018-0216-7"},{"key":"2025111010001683000_2025.09.17.676780v2.47","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01018-x"},{"key":"2025111010001683000_2025.09.17.676780v2.48","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-022-01663-4"},{"key":"2025111010001683000_2025.09.17.676780v2.49","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-022-01507-1"},{"key":"2025111010001683000_2025.09.17.676780v2.50","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104044"},{"key":"2025111010001683000_2025.09.17.676780v2.51","doi-asserted-by":"publisher","DOI":"10.1038\/s41596-020-00432-x"},{"key":"2025111010001683000_2025.09.17.676780v2.52","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/s42003-023-04837-8","article-title":"Morphodynamical cell state description via live-cell imaging trajectory embedding","volume":"6","year":"2023","journal-title":"Commun Biol"},{"key":"2025111010001683000_2025.09.17.676780v2.53","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"2025111010001683000_2025.09.17.676780v2.54","unstructured":"Pedregosa F , Varoquaux G , Gramfort A , Michel V , Thirion B , Grisel O , et al. Scikit-learn: Machine Learning in Python. arXiv [cs.LG]. 2012. Available: http:\/\/arxiv.org\/abs\/1201.0490"},{"key":"2025111010001683000_2025.09.17.676780v2.55","doi-asserted-by":"publisher","DOI":"10.1101\/2023.03.31.534937"},{"key":"2025111010001683000_2025.09.17.676780v2.56","doi-asserted-by":"publisher","DOI":"10.1242\/dev.202251"},{"key":"2025111010001683000_2025.09.17.676780v2.57","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1714723115"},{"key":"2025111010001683000_2025.09.17.676780v2.58","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.3036"},{"key":"2025111010001683000_2025.09.17.676780v2.59","doi-asserted-by":"crossref","unstructured":"Bragantini J , Lange M , Royer L. Large-scale multi-hypotheses cell tracking using ultrametric contours maps. arXiv [cs.CV]. 2023. Available: http:\/\/arxiv.org\/abs\/2308.04526","DOI":"10.1007\/978-3-031-72986-7_3"},{"key":"2025111010001683000_2025.09.17.676780v2.60","doi-asserted-by":"publisher","DOI":"10.7554\/elife.34410"},{"key":"2025111010001683000_2025.09.17.676780v2.61","doi-asserted-by":"publisher","DOI":"10.1038\/s41556-024-01393-z"},{"key":"2025111010001683000_2025.09.17.676780v2.62","doi-asserted-by":"publisher","DOI":"10.1242\/jcs.261887"},{"key":"2025111010001683000_2025.09.17.676780v2.63","doi-asserted-by":"publisher","DOI":"10.1101\/pdb.prot105635"},{"key":"2025111010001683000_2025.09.17.676780v2.64","doi-asserted-by":"publisher","DOI":"10.1242\/jcs.260728"},{"key":"2025111010001683000_2025.09.17.676780v2.65","doi-asserted-by":"crossref","unstructured":"Kaiser T , Ulman V , Rosenhahn B. CHOTA: A higher order accuracy metric for cell tracking. arXiv [cs.CV]. 2024. Available: http:\/\/arxiv.org\/abs\/2408.11571","DOI":"10.1007\/978-3-031-91721-9_8"},{"key":"2025111010001683000_2025.09.17.676780v2.66","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-023-01879-y"},{"key":"2025111010001683000_2025.09.17.676780v2.67","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.453"},{"key":"2025111010001683000_2025.09.17.676780v2.68","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"2025111010001683000_2025.09.17.676780v2.69","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.3392"},{"key":"2025111010001683000_2025.09.17.676780v2.70","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.243"},{"key":"2025111010001683000_2025.09.17.676780v2.71","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.1237"},{"key":"2025111010001683000_2025.09.17.676780v2.72","doi-asserted-by":"crossref","unstructured":"McKinney W . Data Structures for Statistical Computing in Python. Proceedings of the Python in Science Conference. SciPy; 2010. pp. 56\u201361.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"2025111010001683000_2025.09.17.676780v2.73","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"2025111010001683000_2025.09.17.676780v2.74","unstructured":"Lemaitre G , Nogueira F , Aridas CK. Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. arXiv [cs.LG]. 2016. Available: http:\/\/arxiv.org\/abs\/1609.06570"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2025.09.17.676780","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:15:11Z","timestamp":1782278111000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2025.09.17.676780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"references-count":74,"URL":"https:\/\/doi.org\/10.1101\/2025.09.17.676780","relation":{"is-preprint-of":[{"id-type":"doi","id":"10.1038\/s44320-026-00212-x","asserted-by":"subject"}]},"subject":[],"published":{"date-parts":[[2025,9,17]]},"subtype":"preprint"}}