ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2018
Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 86 citations in OpenAlex.
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- Digital Holographic Microscopy for Phenotypic Profiling of Adherent Cells.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Morphological and Optical Characterization of NRAS-Mutant Melanoma Cells and Primary Melanocytes via Quantitative Phase Imaging With Digital Holographic Microscopy.Cell biochemistry and function · 2026Article
- Review
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- Characterization of a Single-Capture Bright-Field and Off-Axis Digital Holographic Microscope for Biological Applications.Sensors (Basel, Switzerland) · 2025Article
- Generative adversarial network model to classify human induced pluripotent stem cell-cardiomyocytes based on maturation level.Scientific reports · 2024Article
- Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry.APL bioengineering · 2023Article
- Morphomigrational description as a new approach connecting cell's migration with its morphology.Scientific reports · 2023Article
- Digital Holographic Microscopy to Assess Cell Behavior.Methods in molecular biology (Clifton, N.J.) · 2023Article
- 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
- Morphodynamics facilitate cancer cells to navigate 3D extracellular matrix.Scientific reports · 2021Article
- Digital holographic microscopy evaluation of dynamic cell response to electroporation.Biomedical optics express · 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
- Effect of tobacco in human oral leukoplakia: a cytomorphometric analysis.Medicine and pharmacy reports · 2020Article
- Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging.Journal of biomedical optics · 2020Article
- Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The noninvasive, fast acquisition of quantitative phase maps using digital holographic microscopy (DHM) allows tracking of rapid cellular motility on transparent substrates. On two-dimensional surfaces in vitro, MDA-MB-231 cancer cells assume several morphologies related to the mode of migration and substrate stiffness, relevant to mechanisms of cancer invasiveness in vivo. The quantitative phase information from DHM may accurately classify adhesive cancer cell subpopulations with clinical relevance. To test this, cells from the invasive breast cancer MDA-MB-231 cell line were cultured on glass, tissue-culture treated polystyrene, and collagen hydrogels, and imaged with DHM followed by epifluorescence microscopy after staining F-actin and nuclei. Trends in cell phase parameters were tracked on the different substrates, during cell division, and during matrix adhesion, relating them to F-actin features. Support vector machine learning algorithms were trained and tested using parameters from holographic phase reconstructions and cell geometric features from conventional phase images, and used to distinguish between elongated and rounded cell morphologies. DHM was able to distinguish between elongated and rounded morphologies of MDA-MB-231 cells with 94% accuracy, compared to 83% accuracy using cell geometric features from conventional brightfield microscopy. This finding indicates the potential of DHM to detect and monitor cancer cell morphologies relevant to cell cycle phase status, substrate adhesion, and motility. © 2017 International Society for Advancement of Cytometry.
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