Publications

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Pathogenic viruses are a primary threat to our national security and to the health and economy of our world. Effective defense strategies to combat viral infection and spread require the development of understanding of the mechanisms that these pathogens use to invade the host cell. We present in this report results of our research into viral particle recognition and fusion to cell membranes and the role that protein affinity and confinement in lipid domains plays in membrane curvature in cellular fusion and fission events. Herein, we describe 1) the assembly of the G attachment protein of Nipah virus using point mutation studies to define its role in viral particle fusion to the cell membrane, 2) how lateral pressure of membrane bound proteins induce curvature in model membrane systems, and 3) the role of membrane curvature in the selective partitioning of molecular receptors and specific affinity of associated proteins.

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Formation of complex nanomaterials would ideally involve single-pot reaction conditions with one reactive site per nanoparticle, resulting in a high yield of incrementally modified or oriented structures. Many studies in nanoparticle functionalization have sought to generate highly uniform nanoparticles with tailorable surface chemistry necessary to produce such conjugates, with limited success. In order to overcome these limitations, we have modified commercially available nanoparticles with multiple potential reaction sites for conjugation with single ssDNAs, proteins, and small unilamellar vesicles. These approaches combined heterobifunctional and biochemical template chemistries with single molecule optical methods for improved control of nanomaterial functionalization. Several interesting analytical results have been achieved by leveraging techniques unique to SNL, and provide multiple paths for future improvements for multiplex nanoparticle synthesis and characterization. Hyperspectral imaging has proven especially useful for assaying substrate immobilized fluorescent particles. In dynamic environments, temporal correlation spectroscopies have been employed for tracking changes in diffusion/hydrodynamic radii, particle size distributions, and identifying mobile versus immobile sample fractions at unbounded dilution. Finally, Raman fingerprinting of biological conjugates has been enabled by resonant signal enhancement provided by intimate interactions with nanoparticles and composite nanoshells.

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Cellular autofluorescence, though ubiquitous when imaging cells and tissues, is often assumed to be small in comparison to the signal of interest. Uniform estimates of autofluorescence intensity obtained from separate control specimens are commonly employed to correct for autofluorescence. While these may be sufficient for high signal-to-background applications, improvements in detector and probe technologies and introduction of spectral imaging microscopes have increased the sensitivity of fluorescence imaging methods, exposing the possibility of effectively probing the low signal-to-background regime. With spectral imaging, reliable monitoring of signals near or even below the noise levels of the microscope is possible if autofluorescence and background signals can be accurately compensated for. We demonstrate the importance of accurate autofluorescence determination and utility of spectral imaging and multivariate analysis methods using a case study focusing on fluorescence confocal spectral imaging of host-pathogen interactions. In this application fluorescent proteins are produced when bacteria invade host cells. Unfortunately the analyte signal is spectrally overlapped and typically weaker than the cellular autofluorescence. In addition to discussing the advantages of spectral imaging for following pathogen invasion, we present the spectral properties of mouse macrophage autofluorescence. The imaging and analysis methods developed are widely applicable to cell and tissue imaging. © 2008 Copyright SPIE - The International Society for Optical Engineering.

This short-term, late-start LDRD examined the effects of nutritional deprivation on the energy harvesting complex in microalgae. While the original experimental plan involved a much more detailed study of temperature and nutrition on the antenna system of a variety of TAG producing algae and their concomitant effects on oil production, time and fiscal constraints limited the scope of the study. This work was a joint effort between research teams at Sandia National Laboratories, New Mexico and California. Preliminary results indicate there is a photosystem response to silica starvation in diatoms that could impact the mechanisms for lipid accumulation.

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Hyperspectral imaging confocal microscopy (HSI-CM) is a powerful tool for the analysis of cellular processes such as the immune response. HSI-CM is a data rich technique that routinely generates two-way data having a spectral domain and an image or concentration domain. Using a variety of modifications to the instrument or experimental protocols, one can readily produce three-way data with HSI-CM. These data are often amenable to trilinear analysis. For example we have used a time series of 18 images acquired during photobleaching of the fluorophores in an effort to identify fluorescence resonance energy transfer (FRET). The resulting images represent intensity as a function of concentration, wavelength and photodegradation in time, to which we apply our techniques of trilinear decomposition. We have successfully employed trilinear decomposition of photobleaching spectral image data from fixed A549 cells transfected with yellow and green fluorescent proteins (YFP and GFP) as molecular probes of cellular proteins involved in the cellular immune response. While useful in the interpretation biological processes, the size of the data generated with the HSI-CM can be difficult to manage computationally. The 208 x 204 x 512 x 18 elements in the image data require careful processing and efficient analysis algorithms. Accordingly, we have implemented fast algorithms that can quickly perform the trilinear decomposition. In this paper we describe how three-way data are produced and the methods we have used to process them. Specifically, we show that co-adding spectra in a spatial neighborhood is a highly effective method for improving the performance of these algorithms without sacrificing resolution. Copyright © 2008 John Wiley & Sons, Ltd.