ArticleThe Journal of cell biology2023
Deep learning techniques and mathematical modeling allow 3D analysis of mitotic spindle dynamics.
Article in The Journal of cell biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Microtubule stiffening by the doublecortin-domain protein ZYG-8 contributes to mitotic spindle orientation during zygote division in Caenorhabditis elegans.PLoS genetics · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology.Briefings in bioinformatics · 2026Review
- Fluorescence recovery in the super-resolution regime reveals subcompartments of 53BP1 foci.Cell reports methods · 2025Article
- Image-based identification and isolation of micronucleated cells to dissect cellular consequences.eLife · 2025Article
- DeepKymoTracker: A tool for accurate construction of cell lineage trees for highly motile cells.PloS one · 2025Article
- Unveiling inter-embryo variability in spindle length over time: Towards quantitative phenotype analysis.PLoS computational biology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal discontinuities in time-lapse movies render methods such as 3D object segmentation and tracking difficult. Here, we present SpinX, a framework for reconstructing gaps between successive image frames by combining deep learning and mathematical object modeling. By incorporating expert feedback through selective annotations, SpinX identifies subcellular structures, despite confounding neighbor-cell information, non-uniform illumination, and variable fluorophore marker intensities. The automation and continuity introduced here allows the precise 3D tracking and analysis of spindle movements with respect to the cell cortex for the first time. We demonstrate the utility of SpinX using distinct spindle markers, cell lines, microscopes, and drug treatments. In summary, SpinX provides an exciting opportunity to study spindle dynamics in a sophisticated way, creating a framework for step changes in studies using time-lapse microscopy.
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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.