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Osteogenic differentiation of human mesenchymal stem cells on electroactive substrates.

ABSTRACT: This study investigates the effect of electroactivity and electrical charge distribution on the biological response of human bone marrow stem cells (hBMSCs) cultured in monolayer on flat poly(vinylidene fluoride), PVDF, substrates. Differences in cell behaviour, including proliferation, expression of multipotency markers CD90, CD105 and CD73, and expression of genes characteristic of different mesenchymal lineages, were observed both during expansion in basal medium before reaching confluence and in confluent cultures in osteogenic induction medium. The crystallisation of PVDF in the electrically neutral α-phase or in the electroactive phase β, both unpoled and poled, has been found to have an important influence on the biological response. In addition, the presence of a permanent positive or negative surface electrical charge distribution in phase β substrates has also shown a significant effect on cell behaviour.

SUBMITTER: Tamano-Machiavello MN

PROVIDER: S-EPMC11004758 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Osteogenic differentiation of human mesenchymal stem cells on electroactive substrates.

Tamaño-Machiavello M N MN Carvalho E O EO Correia D D Cordón L L Lanceros-Méndez S S Sempere A A Sabater I Serra R R Ribelles J L Gómez JLG

Heliyon 20240329 7

This study investigates the effect of electroactivity and electrical charge distribution on the biological response of human bone marrow stem cells (hBMSCs) cultured in monolayer on flat poly(vinylidene fluoride), PVDF, substrates. Differences in cell behaviour, including proliferation, expression of multipotency markers CD90, CD105 and CD73, and expression of genes characteristic of different mesenchymal lineages, were observed both during expansion in basal medium before reaching confluence an ...[more]

PMID: 38601667

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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

Osteogenic differentiation of human mesenchymal stem cells on electroactive substrates.

Publications

Osteogenic differentiation of human mesenchymal stem cells on electroactive substrates.

Similar Datasets

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