Durable Mesenchymal Stem Cell Labelling by Using Polyhedral Superparamagnetic Iron Oxide Nanoparticles

Wang, Hao; Wáng, Yì Xiáng J.; Leung, Ken Cham‐Fai; Au, Doris W.T.; Xuan, Shouhu; Chak, Chun‐Pong; Lee, Simon K M; Sheng, Hui; Zhang, Ge; Qin, Ling; Griffith, James F.; Ahuja, Anil T.

doi:10.1002/chem.200901548

Cited by 59 publications

(64 citation statements)

References 51 publications

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“…Among them, APTES is one of the most frequently used to produce the aminosilane (SiO 2 -NH 2 ) coating to enhance protein and cell adhesion. 27,28 SiO 2 -NH 2 -coated SPIO (SPIO@SiO 2 -NH 2 ) nanoparticles prepared by alkaline hydrolysis of APTES have been reported by the present authors 29 and other groups. [30][31][32] In a previous study, SPIO@SiO 2 -NH 2 nanoparticles were applied to rabbit mesenchymal stem cell (MSC) labeling.…”

Section: Introductionsupporting

confidence: 61%

“…[30][31][32] In a previous study, SPIO@SiO 2 -NH 2 nanoparticles were applied to rabbit mesenchymal stem cell (MSC) labeling. 29 It was shown that SPIO@ SiO 2 -NH 2 nanoparticles had a higher labeling efficiency than SiO 2 -coated SPIO (SPIO@SiO 2 ) nanoparticles. However, whether SPIO@SiO 2 -NH 2 nanoparticles offer a universally higher internalization efficiency than other surface coatings in different mammalian cell lines remains unknown.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced cellular uptake of aminosilane-coated superparamagnetic iron oxide nanoparticles in mammalian cell lines

Zhu¹,

Wáng²,

Leung³

et al. 2012

IJN

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Purpose:To compare the cellular uptake efficiency and cytotoxicity of aminosilane (SiO 2 -NH 2 )-coated superparamagnetic iron oxide (SPIO@SiO 2 -NH 2 ) nanoparticles with three other types of SPIO nanoparticles coated with SiO 2 (SPIO@SiO 2 ), dextran (SPIO@dextran), or bare SPIO in mammalian cell lines. Materials and methods: Four types of monodispersed SPIO nanoparticles with a SPIO core size of 7 nm and an overall size in a range of 7-15 nm were synthesized. The mammalian cell lines of MCF-7, MDA-MB-231, HT-29, RAW264.7, L929, HepG2, PC-3, U-87 MG, and mouse mesenchymal stem cells (MSCs) were incubated with four types of SPIO nanoparticles for 24 hours in the serum-free culture medium Dulbecco's modified Eagle's medium (DMEM) with 4.5 µg/mL iron concentration. The cellular uptake efficiencies of SPIO nanoparticles were compared by Prussian blue staining and intracellular iron quantification. In vitro magnetic resonance imaging of MSC pellets after SPIO labeling was performed at 3 T. The effect of each SPIO nanoparticle on the cell viability of RAW 264.7 (mouse monocyte/macrophage) cells was also evaluated. Results: Transmission electron microscopy demonstrated surface coating with SiO 2 -NH 2 , SiO 2 , and dextran prevented SPIO nanoparticle aggregation in DMEM culture medium. MCF-7, MDA-MB-231, and HT-29 cells failed to show notable iron uptake. For all the remaining six cell lines, Prussian blue staining and intracellular iron quantification demonstrated that SPIO@ SiO 2 -NH 2 nanoparticles had the highest cellular uptake efficiency. SPIO@SiO 2 -NH 2 , bare SPIO, and SPIO@dextran nanoparticles did not affect RAW 264.7 cell viability up to 200 µg Fe/mL, while SPIO@SiO 2 reduced RAW 264.7 cell viability from 10 to 200 µg Fe/mL in a dose-dependent manner. Conclusion: Cellular uptake efficiency of SPIO nanoparticles depends on both the cell type and SPIO surface characteristics. Aminosilane surface coating enhanced the cellular uptake efficiency without inducing cytotoxicity in a number of cell lines.

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Section: Introductionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Enhanced cellular uptake of aminosilane-coated superparamagnetic iron oxide nanoparticles in mammalian cell lines

Zhu¹,

Wáng²,

Leung³

et al. 2012

IJN

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show abstract

“…In addition, there are thousands more in "pending" status. The imaging agents in MICAD encompass radioactive labeled small molecules [15], nanoparticles [16], proteins such as antibodies [36] and fluorescent tagged proteins [18], and labeled cells such as stem cells for tracking homing to tumors [19]. Moreover, there are 170 diverse biological applications corresponding to the imaging agents, ranging from biological transporter imaging, cell tracking, angiogenesis imaging to drug resistance and disease detection [20].…”

Section: Biomedical Imaging Researchmentioning

confidence: 99%

“…Tools have become available for handling the complexity of genotype [8][9][10][11][12], and similar advancements are needed to describe phenotype, especially as derived from imaging [4,[13][14][15][16][17][18][19][20]. Publicly accessible resources that support large image archives support file sharing and have so far not yet merged into a framework that supports the collaborative work needed to meet the potential of quantitative image analysis.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers

et al. 2013

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A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image postprocessing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.

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“…On the information side, various databases store information on ex vivo biomarkers (e.g., Early Detection Research Network Biomarker Database [8], Infectious Disease Biomarker Database) [9]. However, information resources for in vivo biomarkers, specifically quantitative imaging biomarkers, are notably lacking [10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Knowledge Representation Framework for the Statistical Validation of Quantitative Imaging Biomarkers

Buckler¹,

Paik

Ouellette³

et al. 2013

J Digit Imaging

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Quantitative imaging biomarkers are of particular interest in drug development for their potential to accelerate the drug development pipeline. The lack of consensus methods and carefully characterized performance hampers the widespread availability of these quantitative measures. A framework to support collaborative work on quantitative imaging biomarkers would entail advanced statistical techniques, the development of controlled vocabularies, and a service-oriented architecture for processing large image archives. Until now, this framework has not been developed. With the availability of tools for automatic ontology-based annotation of datasets, coupled with image archives, and a means for batch selection and processing of image and clinical data, imaging will go through a similar increase in capability analogous to what advanced genetic profiling techniques have brought to molecular biology. We report on our current progress on developing an informatics infrastructure to store, query, and retrieve imaging biomarker data across a wide range of resources in a semantically meaningful way that facilitates the collaborative development and validation of potential imaging biomarkers by many stakeholders. Specifically, we describe the semantic components of our system, QI-Bench, that are used to specify and support experimental activities for statistical validation in quantitative imaging

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Durable Mesenchymal Stem Cell Labelling by Using Polyhedral Superparamagnetic Iron Oxide Nanoparticles

Cited by 59 publications

References 51 publications

Enhanced cellular uptake of aminosilane-coated superparamagnetic iron oxide nanoparticles in mammalian cell lines

Enhanced cellular uptake of aminosilane-coated superparamagnetic iron oxide nanoparticles in mammalian cell lines

Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers

A Novel Knowledge Representation Framework for the Statistical Validation of Quantitative Imaging Biomarkers

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