Dataset Information

Osteogenic Differentiation of Human Mesenchymal Stem cells in a 3D Woven Scaffold.

ABSTRACT: Fiber-based scaffolds produced by textile manufacturing technology offer versatile materials for tissue engineering applications since a wide range of crucial scaffold parameters, including porosity, pore size and interconnectivity, can be accurately controlled using 3D weaving. In this study, we developed a weavable, bioactive biodegradable composite fiber from poly (lactic acid) (PLA) and hydroxyapatite powder by melt spinning. Subsequently, scaffolds of these fibers were fabricated by 3D weaving. The differentiation of human mesenchymal stem cells (hMSCs) in vitro was studied on the 3D scaffolds and compared with differentiation on 2D substrates having the same material composition. Our data showed that the 3D woven scaffolds have a major impact on hMSCs proliferation and activation. The 3D architecture supports the differentiation of the hMSCs into osteoblast cells and enhances the production of mineralized bone matrix. The present study further confirms that a 3D scaffold promotes hMSCs differentiation into the osteoblast-lineage and bone mineralization.

SUBMITTER: Persson M

PROVIDER: S-EPMC6041290 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Osteogenic Differentiation of Human Mesenchymal Stem cells in a 3D Woven Scaffold.

Persson Maria M Lehenkari Petri P PP Berglin Lena L Turunen Sanna S Finnilä Mikko A J MAJ Risteli Juha J Skrifvars Mikael M Tuukkanen Juha J

Scientific reports 20180711 1

Fiber-based scaffolds produced by textile manufacturing technology offer versatile materials for tissue engineering applications since a wide range of crucial scaffold parameters, including porosity, pore size and interconnectivity, can be accurately controlled using 3D weaving. In this study, we developed a weavable, bioactive biodegradable composite fiber from poly (lactic acid) (PLA) and hydroxyapatite powder by melt spinning. Subsequently, scaffolds of these fibers were fabricated by 3D weav ...[more]

PMID: 29993043

Similar Datasets

Project description: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.

Dataset Information

Osteogenic Differentiation of Human Mesenchymal Stem cells in a 3D Woven Scaffold.

Publications

Osteogenic Differentiation of Human Mesenchymal Stem cells in a 3D Woven Scaffold.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets