ArticleNature methods2025
Cell tracking with accurate error prediction.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Quantitative imaging of dynamic processes in intestinal organoids.Nature protocols · 2026Review
- EpiCure (Epithelial Curation): a versatile and handy tool for curation of epithelial segmentation.bioRxiv : the preprint server for biology · 2026Article
- High-Fidelity Long-term Whole-embryo Lineage and Fate Reconstruction by Iterative Tracking with Error Correction.bioRxiv : the preprint server for biology · 2026Article
- A pipeline for cell migration analysis in live-cell imaging data from human iPSC-derived forebrain assembloids.Frontiers in cell and developmental biology · 2026Article
- Organoid: a promising solution to current challenges in cancer immunotherapy.npj biomedical innovations · 2025Review
- Inferring Cell Differentiation Dynamics with Unobserved Progenitors.bioRxiv : the preprint server for biology · 2025Article
- Following a cell's every move.Nature methods · 2025Article
- Pushing the limits of automated cell tracking.Nature methods · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell tracking is an indispensable tool for studying development by time-lapse imaging. However, existing cell trackers cannot assign confidence to predicted tracks, which prohibits fully automated analysis without manual curation. We present a fundamental advance: an algorithm that combines neural networks with statistical physics to determine cell tracks with error probabilities for each step in the track. From these, we can obtain error probabilities for any tracking feature, from cell cycles to lineage trees, that function like P values in data interpretation. Our method, OrganoidTracker 2.0, greatly speeds up tracking analysis by limiting manual curation to rare low-confidence tracking steps. Importantly, it also enables fully automated analysis by retaining only high-confidence track segments, which we demonstrate by analyzing cell cycles and differentiation events at scale for thousands of cells in multiple intestinal organoids. Our approach brings cell dynamics-based organoid screening within reach and enables transparent reporting of cell-tracking results and associated scientific claims.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.