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1.  Characterization of Time-Course Morphological Features for Efficient Prediction of Osteogenic Potential in Human Mesenchymal Stem Cells 
Biotechnology and Bioengineering  2014;111(7):1430-1439.
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). Biotechnol. Bioeng. 2014;111: 1430–1439.
doi:10.1002/bit.25189
PMCID: PMC4231256  PMID: 24420699
image-based analysis; mesenchymal stem cell; non-invasive analysis; osteogenic differentiation; prediction
2.  Morphology-Based Prediction of Osteogenic Differentiation Potential of Human Mesenchymal Stem Cells 
PLoS ONE  2013;8(2):e55082.
Human bone marrow mesenchymal stem cells (hBMSCs) are widely used cell source for clinical bone regeneration. Achieving the greatest therapeutic effect is dependent on the osteogenic differentiation potential of the stem cells to be implanted. However, there are still no practical methods to characterize such potential non-invasively or previously. Monitoring cellular morphology is a practical and non-invasive approach for evaluating osteogenic potential. Unfortunately, such image-based approaches had been historically qualitative and requiring experienced interpretation. By combining the non-invasive attributes of microscopy with the latest technology allowing higher throughput and quantitative imaging metrics, we studied the applicability of morphometric features to quantitatively predict cellular osteogenic potential. We applied computational machine learning, combining cell morphology features with their corresponding biochemical osteogenic assay results, to develop prediction model of osteogenic differentiation. Using a dataset of 9,990 images automatically acquired by BioStation CT during osteogenic differentiation culture of hBMSCs, 666 morphometric features were extracted as parameters. Two commonly used osteogenic markers, alkaline phosphatase (ALP) activity and calcium deposition were measured experimentally, and used as the true biological differentiation status to validate the prediction accuracy. Using time-course morphological features throughout differentiation culture, the prediction results highly correlated with the experimentally defined differentiation marker values (R>0.89 for both marker predictions). The clinical applicability of our morphology-based prediction was further examined with two scenarios: one using only historical cell images and the other using both historical images together with the patient's own cell images to predict a new patient's cellular potential. The prediction accuracy was found to be greatly enhanced by incorporation of patients' own cell features in the modeling, indicating the practical strategy for clinical usage. Consequently, our results provide strong evidence for the feasibility of using a quantitative time series of phase-contrast cellular morphology for non-invasive cell quality prediction in regenerative medicine.
doi:10.1371/journal.pone.0055082
PMCID: PMC3578868  PMID: 23437049
3.  Hyperthermia using magnetite cationic liposomes for hamster osteosarcoma 
Background
We have developed magnetite cationic liposomes (MCLs) and applied them to local hyperthermia as a mediator. MCLs have a positive charge and generate heat under an alternating magnetic field (AMF) by hysteresis loss. In this study, the effect of hyperthermia using MCLs was examined in an in vivo study of hamster osteosarcoma.
Method
MCLs were injected into the osteosarcoma and then subjected to an AMF.
Results
The tumor was heated at over 42°C, but other normal tissues were not heated as much. Complete regression was observed in 100% of the treated group hamsters, whereas no regression was observed in the control group hamsters. At day 12, the average tumor volume of the treated hamsters was about 1/1000 of that of the control hamsters. In the treated hamsters, no regrowth of osteosarcomas was observed over a period of 3 months after the complete regression.
Conclusion
These results suggest that this treatment is effective for osteosarcoma.
doi:10.1186/1477-044X-2-3
PMCID: PMC400754  PMID: 15040804

Results 1-3 (3)