ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2019
Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
- Digital Holographic Microscopy for Phenotypic Profiling of Adherent Cells.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A self-supervised learning approach for high throughput and high content cell segmentation.Communications biology · 2025Article
- Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks.Scientific reports · 2024Article
- Digital Holographic Microscopy to Assess Cell Behavior.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Review
- Dual-modality digital holographic and polarization microscope to quantify phase and birefringence signals in biospecimens with a complex microstructure.Biomedical optics express · 2022Article
- Keratin 19 maintains E-cadherin localization at the cell surface and stabilizes cell-cell adhesion of MCF7 cells.Cell adhesion & migration · 2021Article
- Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia.Biosensors & bioelectronics · 2021Article
- Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential.Biosensors & bioelectronics · 2021Article
- Morphology, Motility, and Cytoskeletal Architecture of Breast Cancer Cells Depend on Keratin 19 and Substrate.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition.Journal of biomedical optics · 2020Article
- Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging.Journal of biomedical optics · 2020Article
- Automation, Monitoring, and Standardization of Cell Product Manufacturing.Frontiers in bioengineering and biotechnology · 2020Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and reproducible optical phase signatures of cell lines for classification with machine learning and to correlate optical phase parameters to motile behavior. Digital holographic microscopy (DHM) reconstructed phase maps of cells from two pairs of cancer and non-cancer cell lines. Seventeen image parameters were extracted from each cell's phase map, used for linear support vector machine learning, and correlated to scratch wound closure and Boyden chamber chemotaxis. The classification accuracy was between 90% and 100% for the six pairwise cell line comparisons. Several phase parameters correlated with wound closure rate and chemotaxis across the four cell lines. The level of cell confluence in culture affected phase parameters in all cell lines tested. Results indicate that optical phase features of cell lines are a robust set of quantitative data of potential utility for phenotypic screening and prediction of motile behavior. © 2019 International Society for Advancement of Cytometry.
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