Surface-enhanced Raman scattering(SERS)spectroscopy is presented as a sensitive and spe-cific molecular tool for clinical diagnosis and prognosis monitoring of various diseases including cancer.In order for clinical a...Surface-enhanced Raman scattering(SERS)spectroscopy is presented as a sensitive and spe-cific molecular tool for clinical diagnosis and prognosis monitoring of various diseases including cancer.In order for clinical application of SERS technique,an ideal method of bulk synthesis of SERS nanoparticles is necessary to obtain sensitive,stable and highly reproducible Raman signals.In this contribution,we determined the ideal conditions for bulk synthesis of Raman reporter(Ra)molecules embedded silver-gold core-shell nanoparticles(Au@Ra@AgNPs)using hydroquinone as reducing agent of silver nitrate.By using UV-Vis spectroscopy,Raman spectroscopy and transmission electron microscopy(TEM),we found that a 2∶1 ratio of silver nitrate to hydroquinone is ideal for a uniform silver coating with a strong and stable Raman signal.Through stability testing of the optimized Au@Ra@AgNPs over a two-week period,these SERS nanotags were found to be stable with minimal signal change occurred.The sta-bility of antibody linked SERS nanotags is also crucial for cancer and disease diagnosis,thus,we further conjugated the as-prepared SERS nanotags with anti-EpCAM antibody,in which the stability of bioconjugated SERS nanotags was tested over eight days.Both UV-Vis and SERS spectroscopy showed stable absorption and Raman signals on the anti-EpCAM conju-gated SERS nanotags,indicating the great potential of the synthesized SERS nanotags for future applications which require large,reproducible and uniform quantities in order for cancer biomarker diagnosis and monitoring.展开更多
Microarray gene expression measurements are reported, used and archived usually to high numerical precision. However, properties of mRNA molecules, such as their low stability and availability in small copy numbers, a...Microarray gene expression measurements are reported, used and archived usually to high numerical precision. However, properties of mRNA molecules, such as their low stability and availability in small copy numbers, and the fact that measurements correspond to a population of cells, rather than a single cell, makes high precision meaningless. Recent work shows that reducing measurement precision leads to very little loss of information, right down to binary levels. In this paper we show how properties of binary spaces can be useful in making inferences from microarray data. In particular, we use the Tanimoto similarity metric for binary vectors, which has been used effectively in the Chemoinformatics literature for retrieving chemical compounds with certain functional properties. This measure, when incorporated in a kernel framework, helps recover any information lost by quantization. By implementing a spectral clustering framework, we further show that a second reason for high performance from the Tanimoto metric can be traced back to a hitherto unnoticed systematic variability in array data: Probe level uncertainties are systematically lower for arrays with large numbers of expressed genes. While we offer no molecular level explanation for this systematic variability, that it could be exploited in a suitable similarity metric is a useful observation in itself. We further show preliminary results that working with binary data considerably reduces variability in the results across choice of algorithms in the preprocessing stages of microarray analysis.展开更多
基金This work was supported by the Australian Research Council(ARC)through its Centre of Excellence for Nanoscale BioPhotonics(CE140100003)ARC Discovery Projects(DP200102004).
文摘Surface-enhanced Raman scattering(SERS)spectroscopy is presented as a sensitive and spe-cific molecular tool for clinical diagnosis and prognosis monitoring of various diseases including cancer.In order for clinical application of SERS technique,an ideal method of bulk synthesis of SERS nanoparticles is necessary to obtain sensitive,stable and highly reproducible Raman signals.In this contribution,we determined the ideal conditions for bulk synthesis of Raman reporter(Ra)molecules embedded silver-gold core-shell nanoparticles(Au@Ra@AgNPs)using hydroquinone as reducing agent of silver nitrate.By using UV-Vis spectroscopy,Raman spectroscopy and transmission electron microscopy(TEM),we found that a 2∶1 ratio of silver nitrate to hydroquinone is ideal for a uniform silver coating with a strong and stable Raman signal.Through stability testing of the optimized Au@Ra@AgNPs over a two-week period,these SERS nanotags were found to be stable with minimal signal change occurred.The sta-bility of antibody linked SERS nanotags is also crucial for cancer and disease diagnosis,thus,we further conjugated the as-prepared SERS nanotags with anti-EpCAM antibody,in which the stability of bioconjugated SERS nanotags was tested over eight days.Both UV-Vis and SERS spectroscopy showed stable absorption and Raman signals on the anti-EpCAM conju-gated SERS nanotags,indicating the great potential of the synthesized SERS nanotags for future applications which require large,reproducible and uniform quantities in order for cancer biomarker diagnosis and monitoring.
文摘Microarray gene expression measurements are reported, used and archived usually to high numerical precision. However, properties of mRNA molecules, such as their low stability and availability in small copy numbers, and the fact that measurements correspond to a population of cells, rather than a single cell, makes high precision meaningless. Recent work shows that reducing measurement precision leads to very little loss of information, right down to binary levels. In this paper we show how properties of binary spaces can be useful in making inferences from microarray data. In particular, we use the Tanimoto similarity metric for binary vectors, which has been used effectively in the Chemoinformatics literature for retrieving chemical compounds with certain functional properties. This measure, when incorporated in a kernel framework, helps recover any information lost by quantization. By implementing a spectral clustering framework, we further show that a second reason for high performance from the Tanimoto metric can be traced back to a hitherto unnoticed systematic variability in array data: Probe level uncertainties are systematically lower for arrays with large numbers of expressed genes. While we offer no molecular level explanation for this systematic variability, that it could be exploited in a suitable similarity metric is a useful observation in itself. We further show preliminary results that working with binary data considerably reduces variability in the results across choice of algorithms in the preprocessing stages of microarray analysis.
