ArticleeLife2022
DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 40 citations in OpenAlex.
- BudFinder: A Masked Auto-Encoder vision transformer framework for yeast budding detection and lifespan quantification.PLoS computational biology · 2026Article
- Potassium ion homeostasis modulates mitochondrial function.The Journal of cell biology · 2026Article
- Single-cell analyses identify independent aging processes that compete to determine cellular fate in budding yeast.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Cell enlargement drives aging-associated proteome remodeling and shortens replicative lifespan.bioRxiv : the preprint server for biology · 2026Article
- Love-thy-neighbor: Neural networks for tracking and lineage tracing in budding yeast.bioRxiv : the preprint server for biology · 2026Article
- A Framework for Automated Tracking of Morphologically Distinct Cell Populations in Time-Lapse Microscopy....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2026Article
- A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.Frontiers in aging · 2026Review
- Love-thy-neighbor: neural networks for tracking and lineage tracing in budding yeast.Bioinformatics advances · 2026Article
- Cell tracking with accurate error prediction.Nature methods · 2025Article
- Deep learning detection of dynamic exocytosis events in fluorescence TIRF microscopy.PLoS computational biology · 2025Article
- Preliminary Study on Sensor-Based Detection of an Adherent Cell's Pre-Detachment Moment in a MPWM Microfluidic Extraction System.Sensors (Basel, Switzerland) · 2025Article
- CeDaD-a novel assay for simultaneous tracking of cell death and division in a single population.Cell death discovery · 2025Article
- Equivalent Circuit Modeling and Analysis for Microfluidic Electrical Impedance Monitoring of Single-Cell Growth.Biosensors · 2025Article
- High-Throughput Measurement of Single-Fission Yeast Cell Volume Using Fluorescence Exclusion.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Determining growth rates from bright-field images of budding cells through identifying overlaps.eLife · 2023Article
- DeXtrusion: automatic recognition of epithelial cell extrusion through machine learning in vivo.Development (Cambridge, England) · 2023Article
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations.PeerJ. Computer science · 2023Article
- Nuclear pore complex acetylation regulates mRNA export and cell cycle commitment in budding yeast.The EMBO journal · 2022Article
- A Microfluidic Platform for Tracking Individual Cell Dynamics during an Unperturbed Nutrients Exhaustion.Bio-protocol · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamical biological processes. So far, strong limitations in the ability to quantitatively analyze single-cell trajectories have prevented large-scale investigations to assess the dynamics of entry into replicative senescence in yeast. Here, we have developed DetecDiv, a microfluidic-based image acquisition platform combined with deep learning-based software for high-throughput single-cell division tracking. We show that DetecDiv can automatically reconstruct cellular replicative lifespans with high accuracy and performs similarly with various imaging platforms and geometries of microfluidic traps. In addition, this methodology provides comprehensive temporal cellular metrics using time-series classification and image semantic segmentation. Last, we show that this method can be further applied to automatically quantify the dynamics of cellular adaptation and real-time cell survival upon exposure to environmental stress. Hence, this methodology provides an all-in-one toolbox for high-throughput phenotyping for cell cycle, stress response, and replicative lifespan assays.
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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.