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Stem Cells Dev. 2008 Feb;17(1):29-39. doi: 10.1089/scd.2007.0085.

Combinatorial signaling microenvironments for studying stem cell fate.

Author information

1
Department of Bioengineering and Medicine, University of California-San Diego, La Jolla, CA 92093-0412, USA.

Abstract

Extracellular matrix (ECM) and growth factor signaling networks are known to interact in a complex manner. Therefore, reductionist approaches that test the cellular response to individual ECM components and growth factors cannot be used to predict the response to more complex mixtures without knowledge of the underlying signaling network. To address this challenge, we have developed a technology platform to experimentally probe the interactions of ECM components and soluble growth factors on stem cell fate. We present a multiwell microarray platform that allows 1200 simultaneous experiments on 240 unique signaling environments. Mixtures of extracellular matrix (fibronectin, laminin, collagen I, collagen III, collagen IV) are arrayed using a robotic spotter and arranged in a multiwell format. Embryonic stem (ES) cells adhere to ECM spots and are cultured in mixtures of soluble factors [wnt3a, activin A, bone morphogenetic protein-4 (BMP-4), and fibroblast growth factor-4 (FGF-4)]. Differentiation along the cardiac lineage is monitored by myosin heavy chain-alpha-green fluorescent protein (MHC alpha-GFP) reporter expression as compared to growth by monitoring nuclear DNA, and both signals are quantified using a confocal microarray scanner. In developing the platform, we characterized the amount of deposited protein, the fluorescent readout of GFP expression and DNA content, and the use of a laser-based scanner as compared to fluorescent microscopy for data acquisition. The effects of growth factors on growth and differentiation are consistent with previously reported literature, and preliminary evidence of interactive signaling is illuminated. This versatile technique is compatible with virtually any set of insoluble and soluble cues, leverages existing software and hardware, and represents a step toward developing the 'systems biology' of stem cells.

PMID:
18271698
DOI:
10.1089/scd.2007.0085
[Indexed for MEDLINE]

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