Controlling differentiation of adipose-derived stem cells using combinatorial graphene hybrid-pattern arrays

Tae Hyung Kim, Shreyas Shah, Letao Yang, Perry T. Yin, Md Khaled Hossain, Brian Conley, Jeong Woo Choi, Ki Bum Lee

Research output: Contribution to journalArticlepeer-review

133 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3780-3790
Number of pages11
JournalACS Nano
Issue number4
StatePublished - Apr 28 2015

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Engineering(all)
  • Physics and Astronomy(all)


  • adipose-derived stem cells
  • cell morphology
  • combinatorial pattern
  • differentiation
  • graphene arrays


Dive into the research topics of 'Controlling differentiation of adipose-derived stem cells using combinatorial graphene hybrid-pattern arrays'. Together they form a unique fingerprint.

Cite this