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Review | Open Access

GO-Fe3O4 Nanoparticle Composite for Selective Targeting of Cancer Cells

Venkatesha Narayanaswamy1( ), Yasrib Qurishi2, Chandan Srivastava1
Department of Materials Engineering, Indian Institute of Science, Bangalore, India
Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore, India
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Abstract

Graphene oxide (GO) based nanocomposites have attracted lot of attention in the biomedical field, especially for the diagnosis and treatment of tumors. Using invitro studies, recent reports have illustrated the ability of graphene oxide for selective killing of several cancer cell lines. Coupling of the anticancer property of graphene oxide with magnetic nanoparticles makes the graphene oxide-nanoparticle composite a potential material for diagnosis and treatment of cancer. In this work, GO-Fe3O4 nanoparticle composite was synthesized by the co-precipitation method. The GO-Fe3O4 nanoparticle composite was thoroughly analyzed for their potential towards killing of cancer cell lines by various assays like MTT, cell cycle analysis by flow cytometry, flow cytometric analysis of apoptosis and necrosis, apoptotic characterization of HL-60 cells using fluorescence microscopy, and flow cytometric measurement of intracellular peroxides (ROS). All these biological end-points indicated cell death by apoptosis. Here, we report that GO-Fe3O4 composites interact with HeLa and cause dose dependent cytotoxicity which robustly induced cell cycle arrest, annexin V-FITC staining and reactive oxygen species generation.

Keywords

Graphene oxideAnticancerApoptosisMTTCytometry

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Nano Biomedicine and Engineering
Volume 9 Issue 1,
March 2017
Pages 96-102
DOI: 10.5101/nbe.v9i1.p96-102
Cite this article:
Narayanaswamy V, Qurishi Y, Srivastava C. GO-Fe3O4 Nanoparticle Composite for Selective Targeting of Cancer Cells. Nano Biomedicine and Engineering, 2017, 9(1): 96-102. https://doi.org/10.5101/nbe.v9i1.p96-102

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Received: 06 March 2017
Accepted: 30 March 2017
Published: 31 March 2017
© 2017 Venkatesha Narayanaswamy, Yasrib Qurishi, and Chandan Srivastava.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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