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Time-resolved surface-enhanced coherent sensing of nanoscale molecular complexes.

ABSTRACT: Nanoscale real-time molecular sensing requires large signal enhancement, small background, short detection time and high spectral resolution. We demonstrate a new vibrational spectroscopic technique which satisfies all of these conditions. This time-resolved surface-enhanced coherent anti-Stokes Raman scattering (tr-SECARS) spectroscopy is used to detect hydrogen-bonded molecular complexes of pyridine with water in the near field of gold nanoparticles with large signal enhancement and a fraction of a second collection time. Optimal spectral width and time delays of ultrashort laser pulses suppress the surface-enhanced non-resonant background. Time-resolved signals increase the spectral resolution which is limited by the width of the probe pulse and allow measuring nanoscale vibrational dephasing dynamics. This technique combined with quantum chemistry simulations may be used for the investigation of complex mixtures at the nanoscale and surface environment of artificial nanostructures and biological systems.

SUBMITTER: Voronine DV

PROVIDER: S-EPMC3506977 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Time-resolved surface-enhanced coherent sensing of nanoscale molecular complexes.

Voronine Dmitri V DV Sinyukov Alexander M AM Hua Xia X Wang Kai K Jha Pankaj K PK Munusamy Elango E Wheeler Steven E SE Welch George G Sokolov Alexei V AV Scully Marlan O MO

Scientific reports 20121127

Nanoscale real-time molecular sensing requires large signal enhancement, small background, short detection time and high spectral resolution. We demonstrate a new vibrational spectroscopic technique which satisfies all of these conditions. This time-resolved surface-enhanced coherent anti-Stokes Raman scattering (tr-SECARS) spectroscopy is used to detect hydrogen-bonded molecular complexes of pyridine with water in the near field of gold nanoparticles with large signal enhancement and a fraction ...[more]

PMID: 23189240

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Project description:Metabolomics is an emerging field of cell biology that aims at the comprehensive identification of metabolite levels in biological fluids or cells in a specific functional state. Currently, the major tools for determining metabolite concentrations are mass spectrometry coupled with chromatographic techniques and nuclear magnetic resonance, which are expensive, time consuming and destructive for the samples. Here, we report a time resolved approach to monitor metabolite dynamics in cell cultures, based on Surface Enhanced Raman Scattering (SERS). This method is label-free, easy to use and provides the opportunity to simultaneously study a broad range of molecules, without the need to process the biological samples. As proof of concept, NIH/3T3 cells were cultured in vitro, and the extracellular medium was collected at different time points to be analyzed with our engineered SERS substrates. By identifying individual peaks of the Raman spectra, we showed the simultaneous detection of several components of the conditioned medium, such as L-tyrosine, L-tryptophan, glycine, L-phenylalanine, L-histidine and fetal bovine serum proteins, as well as their intensity changes during time. Furthermore, analyzing the whole Raman data set with the Principal Component Analysis (PCA), we demonstrated that the Raman spectra collected at different days of culture and clustered by similarity, described a well-defined trajectory in the principal component plot. This approach was then utilized to determine indirectly the functional state of the macrophage cell line Raw 264.7, stimulated with the lipopolysaccharide (LPS) for 24 hours. The collected spectra at different time points, clustered by the PCA analysis, followed a well-defined trajectory, corresponding to the functional change of cells toward the activated pro-inflammatory state induced by the LPS. This study suggests that our engineered SERS surfaces can be used as a versatile tool both for the characterization of cell culture conditions and the functional state of cells over time.

Project description:Prior image constrained compressed sensing (PICCS) is an image reconstruction framework that takes advantage of a prior image to improve the image quality of CT reconstructions. An interesting question that remains to be investigated is whether or not the introduction of a statistical model of the photon detection in the PICCS reconstruction framework can improve the performance of the algorithm when dealing with high noise projection datasets. The goal of the research presented in this paper is to characterize the noise properties of images reconstructed using PICCS with and without statistical modeling. This paper investigates these properties in the clinical context of time-resolved contrast-enhanced CT.Both numerical phantom studies and an Institutional Review Board approved human subject study were used in this research. The conventional filtered backprojection (FBP), and PICCS with and without the statistical model were applied to each dataset. The prior image used in PICCS was generated by averaging over FBP reconstructions from different time frames of the time-resolved CT exam, thus reducing the noise level. Numerical studies were used to evaluate if the noise characteristics are altered for varying levels of noise, as well as for different object shapes. The dataset acquired in vivo was used to verify that the conclusions reached from numerical studies translate adequately to a clinical case. The results were analyzed using a variety of qualitative and quantitative metrics such as the universal image quality index, spatial maps of the noise standard deviations, the noise uniformity, the noise power spectrum, and the model-observer detectability.The noise characteristics of PICCS were shown to depend on the noise level contained in the data, the level of eccentricity of the object, and whether or not the statistical model was applied. Most differences in the characteristics were observed in the regime of low incident x-ray fluence. No substantial difference was observed between PICCS with and without statistics in the high fluence domain. Objects with a semi-major axis ratio below 0.85 were more accurately reconstructed with lower noise using the statistical implementation. Above that range, for mostly circular objects, the PICCS implementation without the statistical model yielded more accurate images and a lower noise level. At all levels of eccentricity, the noise spatial distribution was more uniform and the model-observer detectability was greater for PICCS with the statistical model. The human subject study was consistent with the results obtained using numerical simulations.For mildly eccentric objects in the low noise regime, PICCS without the noise model yielded equal or better noise level and image quality than the statistical formulation. However, in a vast majority of cases, images reconstructed using statistical PICCS have a noise power spectrum that facilitated the detection of model lesions. The inclusion of a statistical model in the PICCS framework does not always result in improved noise characteristics.

Dataset Information

Time-resolved surface-enhanced coherent sensing of nanoscale molecular complexes.

Publications

Time-resolved surface-enhanced coherent sensing of nanoscale molecular complexes.

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