ArticleBiotechnology and bioengineering2014
Characterization of time-course morphological features for efficient prediction of osteogenic potential in human mesenchymal stem cells.
Article in Biotechnology and bioengineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Application of Machine Learning in Predicting Osteogenic Differentiation of Mesenchymal Stem Cells.Bioengineering (Basel, Switzerland) · 2025Review
- Deep Learning Model for Predicting Airway Organoid Differentiation.Tissue engineering and regenerative medicine · 2023Article
- Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs).Frontiers in cell and developmental biology · 2023Article
- Article
- Morphological heterogeneity description enabled early and parallel non-invasive prediction of T-cell proliferation inhibitory potency and growth rate for facilitating donor selection of human mesenchymal stem cells.Inflammation and regeneration · 2022Article
- Article
- In-process evaluation of culture errors using morphology-based image analysis.Regenerative therapy · 2018Article
- Enhanced Differentiation of Human Preosteoblasts on Electrospun Blend Fiber Mats of Polydioxanone and Anionic Sulfated Polysaccharides.ACS biomaterials science & engineering · 2017Article
- Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues.Biomaterials · 2016Article
- Parametric analysis of colony morphology of non-labelled live human pluripotent stem cells for cell quality control.Scientific reports · 2016Article
- Development of a Monitoring Method for Nonlabeled Human Pluripotent Stem Cell Growth by Time-Lapse Image Analysis.Stem cells translational medicine · 2015Article
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8 authors.
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Abstract
Human bone marrow mesenchymal stem cells (hBMSCs) represents one of the most frequently applied cell sources for clinical bone regeneration. To achieve the greatest therapeutic effect, it is crucial to evaluate the osteogenic differentiation potential of the stem cells during their culture before the implantation. However, the practical evaluation of stem cell osteogenicity has been limited to invasive biological marker analysis that only enables assaying a single end-point. To innovate around invasive quality assessments in clinical cell therapy, we previously explored and demonstrated the positive predictive value of using time-course images taken during differentiation culture for hBMSC bone differentiation potential. This initial method establishes proof of concept for a morphology-based cell evaluation approach, but reveals a practical limitation when considering the need to handle large amounts of image data. In this report, we aimed to scale-down our proposed method into a more practical, efficient modeling scheme that can be more broadly implemented by physicians on the frontiers of clinical cell therapy. We investigated which morphological features are critical during the osteogenic differentiation period to assure the performance of prediction models with reduced burden on image acquisition. To our knowledge, this is the first detailed characterization that describes both the critical observation period and the critical number of time-points needed for morphological features to adequately model osteogenic potential. Our results revealed three important observations: (i) the morphological features from the first 3 days of differentiation are sufficiently informative to predict bone differentiation potential, both activities of alkaline phosphatase and calcium deposition, after 3 weeks of continuous culture; (ii) intervals of 48 h are sufficient for measuring critical morphological features; and (iii) morphological features are most accurately predictive when early morphological features from the first 3 days of differentiation are combined with later features (after 10 days of differentiation).
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