Dataset Information

A combinatorial cell-laden gel microarray for inducing osteogenic differentiation of human mesenchymal stem cells.

ABSTRACT: Development of three dimensional (3D) microenvironments that direct stem cell differentiation into functional cell types remains a major challenge in the field of regenerative medicine. Here, we describe a new platform to address this challenge by utilizing a robotic microarray spotter for testing stem cell fates inside various miniaturized cell-laden gels in a systematic manner. To demonstrate the feasibility of our platform, we evaluated the osteogenic differentiation of human mesenchymal stem cells (hMSCs) within combinatorial 3D niches. We were able to identify specific combinations, that enhanced the expression of osteogenic markers. Notably, these 'hit' combinations directed hMSCs to form mineralized tissue when conditions were translated to 3D macroscale hydrogels, indicating that the miniaturization of the experimental system did not alter stem cell fate. Overall, our findings confirmed that the 3D cell-laden gel microarray can be used for screening of different conditions in a rapid, cost-effective, and multiplexed manner for a broad range of tissue engineering applications.

SUBMITTER: Dolatshahi-Pirouz A

PROVIDER: S-EPMC3905276 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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A combinatorial cell-laden gel microarray for inducing osteogenic differentiation of human mesenchymal stem cells.

Dolatshahi-Pirouz Alireza A Nikkhah Mehdi M Gaharwar Akhilesh K AK Hashmi Basma B Guermani Enrico E Aliabadi Hamed H Camci-Unal Gulden G Ferrante Thomas T Foss Morten M Ingber Donald E DE Khademhosseini Ali A

Scientific reports 20140129

Development of three dimensional (3D) microenvironments that direct stem cell differentiation into functional cell types remains a major challenge in the field of regenerative medicine. Here, we describe a new platform to address this challenge by utilizing a robotic microarray spotter for testing stem cell fates inside various miniaturized cell-laden gels in a systematic manner. To demonstrate the feasibility of our platform, we evaluated the osteogenic differentiation of human mesenchymal stem ...[more]

PMID: 24473466

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Project description:Hydroxyapatite (HA), the principal component of bone mineral, shows osteoconductive properties when employed for coating metal implants as well as scaffold materials in synthetic bone grafts. With the goal of providing this material with osteoinductive capabilities to promote faster bone regeneration, we show an easy approach to functionalize HA implant surfaces and enrich them with osteoinductive properties by the use of HA-binding modular peptides. The modular peptides are designed as a combination of two domains, an HA-binding peptide motif and an osteogenic peptide motif derived from the osteogenic growth peptide (OGP) or bone morphometric protein 7 (BMP-7). To identify the best HA-binding peptide, several nature-inspired peptides derived from natural bone extracellular matrix proteins (bone sialoprotein, osteonectin, osteocalcin, and salivarin statherin) were compared for HA-binding activity, revealing concentration-dependent and incubation-time-dependent behaviours. We discovered that a Poly-E heptamer (E7) is the best HA-binding peptide, and thus combined it with a second osteogenic peptidic domain to create an osteoinductive modular peptide. After binding/release characterization, we found that the addition of the second osteogenic peptide domain did not change the binding profile of the modular peptides and caused only a slight change in their release kinetics. Mesenchymal stem cells (MSCs) were cultured on the HA substrates functionalized with modular peptides, and cell adhesion, proliferation, and differentiation in a basal medium (i.e., without any osteogenic supplements) were investigated. Gene expression data clearly showed that MSCs were committed to differentiate into osteoblasts in the presence of the modular peptides. HA discs functionalized with the E7 BMP-7 modular peptide showed the best capability in inducing the osteogenic differentiation of MSCs among all modular peptides studied. The modular peptides can easily be used to functionalize the HA implants through its constituent HA-binding motif, leaving the osteogenic peptide motif protruding from the surface for inducing osteogenesis. Our work opens up a new approach to the formulation of new bioactive HA coatings and implants for bone and dental repair.

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

A combinatorial cell-laden gel microarray for inducing osteogenic differentiation of human mesenchymal stem cells.

Publications

A combinatorial cell-laden gel microarray for inducing osteogenic differentiation of human mesenchymal stem cells.

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