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Geometric control of human stem cell morphology and differentiation.

ABSTRACT: During tissue morphogenesis, stem cells and progenitor cells migrate, proliferate, and differentiate, with striking changes in cell shape, size, and acting mechanical stresses. The local cellular function depends on the spatial distribution of cytokines as well as local mechanical microenvironments in which the cells reside. In this study, we controlled the organization of human adipose derived stem cells using micro-patterning technologies, to investigate the influence of multi-cellular form on spatial distribution of cellular function at an early stage of cell differentiation. The underlying role of cytoskeletal tension was probed through drug treatment. Our results show that the cultivation of stem cells on geometric patterns resulted in pattern- and position-specific cell morphology, proliferation and differentiation. The highest cell proliferation occurred in the regions with large, spreading cells (such as the outer edge of a ring and the short edges of rectangles). In contrast, stem cell differentiation co-localized with the regions containing small, elongated cells (such as the inner edge of a ring and the regions next to the short edges of rectangles). The application of drugs that inhibit the formation of actomyosin resulted in the lack of geometrically specific differentiation patterns. This study confirms the role of substrate geometry on stem cell differentiation, through associated physical forces, and provides a simple and controllable system for studying biophysical regulation of cell function.

SUBMITTER: Wan LQ

PROVIDER: S-EPMC4124726 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Geometric control of human stem cell morphology and differentiation.

Wan Leo Q LQ Kang Sylvia M SM Eng George G Grayson Warren L WL Lu Xin L XL Huo Bo B Gimble Jeffrey J Guo X Edward XE Mow Van C VC Vunjak-Novakovic Gordana G

Integrative biology : quantitative biosciences from nano to macro 20100722 7-8

During tissue morphogenesis, stem cells and progenitor cells migrate, proliferate, and differentiate, with striking changes in cell shape, size, and acting mechanical stresses. The local cellular function depends on the spatial distribution of cytokines as well as local mechanical microenvironments in which the cells reside. In this study, we controlled the organization of human adipose derived stem cells using micro-patterning technologies, to investigate the influence of multi-cellular form on ...[more]

PMID: 20652175

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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

Geometric control of human stem cell morphology and differentiation.

Publications

Geometric control of human stem cell morphology and differentiation.

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