ArticleACS photonics2022
Cell Cycle Stage Classification Using Phase Imaging with Computational Specificity.
Article in ACS photonics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Cell Cycle Control of Nuclear Metabolism Couples Phosphatidylinositol Signaling to Histone Methylation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Profiling extracellular matrix-driven heterogeneity of single cell migration and morphology.Scientific reports · 2026Article
- Temporal variance mapping with machine learning for label-free 3D chromatin imaging using optical interferometric microscopy.Biomedical optics express · 2026Article
- A deep learning approach for time-consistent cell cycle phase prediction from microscopy data.PLoS computational biology · 2025Article
- Predicting cell cycle stage from 3D single-cell nuclear-stained images.Life science alliance · 2025Article
- Cell-TIMP: Cellular Trajectory Inference Based on Morphological Parameters.Nano letters · 2025Article
- A novel method for estrous cycle staging using supervised object detection.NPP - digital psychiatry and neuroscience · 2025Article
- Measuring Molecular Mass Densities at Subcellular Resolution Using Optical Diffraction Tomography.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Capturing cell morphology dynamics with high temporal resolution using single-shot quantitative phase gradient imaging.Journal of biomedical optics · 2024Article
- Perspective on quantitative phase imaging to improve precision cancer medicine.Journal of biomedical optics · 2024Article
- CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.Journal of biomedical optics · 2024Article
- Continuous distribution of cancer cells in the cell cycle unveiled by AI-segmented imaging of 37,000 HeLa FUCCI cells.Heliyon · 2024Article
- EVATOM: an optical, label-free, machine learning assisted embryo health assessment tool.Communications biology · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Panoramic quantitative phase imaging of adherent live cells in a microfluidic environment.Biomedical optics express · 2023Article
- Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry.APL bioengineering · 2023Article
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Digital staining facilitates biomedical microscopy.Frontiers in bioinformatics · 2023Article
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Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Traditional methods for cell cycle stage classification rely heavily on fluorescence microscopy to monitor nuclear dynamics. These methods inevitably face the typical phototoxicity and photobleaching limitations of fluorescence imaging. Here, we present a cell cycle detection workflow using the principle of phase imaging with computational specificity (PICS). The proposed method uses neural networks to extract cell cycle-dependent features from quantitative phase imaging (QPI) measurements directly. Our results indicate that this approach attains very good accuracy in classifying live cells into G1, S, and G2/M stages, respectively. We also demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as cell population distribution across different stages of the cell cycle. We envision that the proposed method can become a nondestructive tool to analyze cell cycle progression in fields ranging from cell biology to biopharma applications.
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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.