Dataset Information

Differentiation Potential of Mesenchymal Stem Cells Is Related to Their Intrinsic Mechanical Properties.

ABSTRACT:

Purpose

The differentiation properties of stem cells are not yet fully understood due to their close association with multiple environmental and extrinsic factors. This study investigates the differentiation properties of mesenchymal stem cells (MSCs) and correlates them with their intrinsic mechanical properties.

Methods

A total of 3 different types of MSCs, namely bone marrow-derived MSCs (BMSCs), umbilical cord-derived MSCs (UCSCs), and adipose-derived MSCs (ADSCs) were evaluated. These 3 MSCs were individually differentiated into adipocytes and osteoblasts for 3 weeks. The mechanical properties of the MSCs and differentiated cells were determined by atomic force microscopy.

Results

ADSCs showed the greatest ability to differentiate into adipocytes, followed by BMSCs and UCSCs. While UCSCs differentiated readily into osteoblasts, BMSCs and ADSCs were less likely to undergo this differentiation. UCSCs were the "hardest" cells, while ADSCs were the "softest." The cells differentiated from "hard" MSCs were stiffer than the cells differentiated from "soft" MSCs, irrespective of lineage specification.

Conclusions

The differentiation ability of MSCs and the mechanical properties of the differentiated cells were closely linked. However, there were no significant correlations regarding changes in the mechanical properties between the nuclear region and the cytoplasm during differentiation.

SUBMITTER: Han I

PROVIDER: S-EPMC5426435 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Publications

Differentiation Potential of Mesenchymal Stem Cells Is Related to Their Intrinsic Mechanical Properties.

Han Ihn I Kwon Byung-Su BS Park Hun-Kuk HK Kim Kyung Sook KS

International neurourology journal 20170421 Suppl 1

<h4>Purpose</h4>The differentiation properties of stem cells are not yet fully understood due to their close association with multiple environmental and extrinsic factors. This study investigates the differentiation properties of mesenchymal stem cells (MSCs) and correlates them with their intrinsic mechanical properties.<h4>Methods</h4>A total of 3 different types of MSCs, namely bone marrow-derived MSCs (BMSCs), umbilical cord-derived MSCs (UCSCs), and adipose-derived MSCs (ADSCs) were evaluat ...[more]

PMID: 28446012

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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

Differentiation Potential of Mesenchymal Stem Cells Is Related to Their Intrinsic Mechanical Properties.

Purpose

Methods

Results

Conclusions

Publications

Differentiation Potential of Mesenchymal Stem Cells Is Related to Their Intrinsic Mechanical Properties.

Similar Datasets

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