Controlling Differentiation of Neural Stem Cells Using Extracellular Matrix Protein Patterns

Solanki, Aniruddh; Shah, Shreyas; Memoli, Kevin A.; In, Insik; Hong, Seok Hoon; Lee, Ki‐Bum

doi:10.1002/smll.201001341

Cited by 84 publications

(91 citation statements)

References 37 publications

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“…A thiol-modified passivation molecule (HS-C 11 –PEG 4 –OH) was used prior to the cell seeding to prevent cell adhesion to areas of the substrate that lack NGO, which is critical for the NGO pattern-mediated manipulation of cell morphology. 15 In this way, the morphology of the hADMSCs was successfully altered, solely based on the geometry of the underlying NGO micropatterns (Supporting Information Figure 7). In particular, we observed that cell spreading was the highest on NGO line patterns.…”

Section: Resultsmentioning

confidence: 99%

“…3,13–16 As a means to systematically control the biophysical microenvironment of cells, nano- and microstructures/patterns have emerged as an interesting approach to direct stem cell differentiation without the need for any genetic modification and/or external stimulation. 15,17,18 For example, it has been shown that micropatterns, ranging on the scale of a few micrometers to a hundred micrometers, are effective in steering stem cell fate. In particular, this strategy entails manipulating the cellular morphology by using ECM proteins arranged in geometric orientations that can mimic the cellular arrangements found in the native tissues.…”

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confidence: 99%

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Controlling Differentiation of Adipose-Derived Stem Cells Using Combinatorial Graphene Hybrid-Pattern Arrays

et al. 2015

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Control of stem cell fate by modulating biophysical cues (e.g., micropatterns, nanopatterns, elasticity and porosity of the substrates) has emerged as an attractive approach in stem cell-based research. Here, we report a method for fabricating combinatorial patterns of graphene oxide (GO) to effectively control the differentiation of human adipose-derived mesenchymal stem cells (hADMSCs). In particular, GO line patterns were highly effective for modulating the morphology of hADMSCs, resulting in enhanced differentiation of hADMSCs into osteoblasts. Moreover, by generating GO grid patterns, we demonstrate the highly efficient conversion of mesodermal stem cells to ectodermal neuronal cells (conversion efficiency = 30%), due to the ability of the grid patterns to mimic interconnected/elongated neuronal networks. This work provides an early demonstration of developing combinatorial graphene hybrid-pattern arrays for the control of stem cell differentiation, which can potentially lead to more effective stem cell-based treatment of incurable diseases/disorders.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Controlling Differentiation of Adipose-Derived Stem Cells Using Combinatorial Graphene Hybrid-Pattern Arrays

et al. 2015

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“…This platform facilitates the study of almost any insoluble ligand in a combinatorial fashion. Though we have only discussed a few examples, there are several microfl uidic devices that have been developed to cultivate stem cells and to investigate the relationship between adhesive ligands and stem cell regulation (Chin et al ., 2004;Lanfer et al ., 2009;Solanki et al ., 2010).…”

Section: Adhesive Ligands (Extracellular Matrix) Regulationmentioning

confidence: 94%

Microfluidic devices for stem cell analysis

Kang¹,

Lu²,

Zhang

et al. 2013

Microfluidic Devices for Biomedical Applications

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“…19 For example, nanostructured surfaces, formed by CNTs, graphene, and SiNW, promote cellular adhesion, spreading, and direct axonal growth, even in the absence of adhesion molecules, such as poly lysine, laminin, fibronectine, and so on. 81,85,86,96,102 Electrical recording from the NW-FET arrays have been reported from various cells such as neurons, cardiomyocytes, and so on. 86 For instance, Patolsky et al 86 reported that SiNW-FET arrays integrated with individual axons, and dendrites of live neurons can be used to record changes in the extracellular field in a highly sensitive manner with stimulation and inhibition of neuronal signal propagation as shown in Fig.…”

Section: Cell-based Nanobiosensormentioning

confidence: 99%

Nanomaterial-Based Biosensor as an Emerging Tool for Biomedical Applications

2011

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The combination of nanomaterials and biological sensing elements to selectively recognize chemical or biological molecules has resulted in the development of novel nanobiosensors. Nanobiosensors offer several important advantages over conventional biological procedures, and could have a significant impact on humankind. Hence, the momentum toward building miniaturized, reliable, sensitive, and selective sensing instruments has focused on combining nanomaterials with biomolecules for detection of a wide range of analytes. In this article, we present an overview of the various nanomaterial-based biosensors that utilize different biological recognition elements for biomedical applications. In this review, several types of nanomaterial-based biosensors along with their applications are discussed, including the latest developments in the field of nanobiosensors for biomedical applications.

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Controlling Differentiation of Neural Stem Cells Using Extracellular Matrix Protein Patterns

Cited by 84 publications

References 37 publications

Controlling Differentiation of Adipose-Derived Stem Cells Using Combinatorial Graphene Hybrid-Pattern Arrays

Controlling Differentiation of Adipose-Derived Stem Cells Using Combinatorial Graphene Hybrid-Pattern Arrays

Microfluidic devices for stem cell analysis

Nanomaterial-Based Biosensor as an Emerging Tool for Biomedical Applications

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