Publications

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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.

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We have developed a system to measure the directional thermal emission from a surface, and in turn, calculate its emissivity. This approach avoids inaccuracies sometimes encountered with the traditional method for calculating emissivity, which relies upon subtracting the measured total reflectivity and total transmissivity from unity. Typical total reflectivity measurements suffer from an inability to detect backscattered light, and may not be accurate for high angles of incidence. Our design allows us to vary the measurement angle (θ) from near-normal to ∼80°, and can accommodate samples as small as 7 mm on a side by controlling the sample interrogation area. The sample mount is open-backed to eliminate shine-through, can be heated up to 200°C, and is kept under vacuum to avoid oxidizing the sample. A cold shield reduces the background noise and stray signals reflected off the sample. We describe the strengths, weaknesses, trade-offs, and limitations of our system design, data analysis methods, the measurement process, and present the results of our validation of this Variable-Angle Directional Emissometer.

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Production of renewable biofuels to displace fossil fuels currently consumed in the transportation sector is a pressing multi-agency national priority. Currently, nearly all fuel ethanol is produced from corn-derived starch. Dedicated 'energy crops' and agricultural waste are preferred long-term solutions for renewable, cheap, and globally available biofuels as they avoid some of the market pressures and secondary greenhouse gas emission challenges currently facing corn ethanol. These sources of lignocellulosic biomass are converted to fermentable sugars using a variety of chemical and thermochemical pretreatments, which disrupt cellulose and lignin cross-links, allowing exogenously added recombinant microbial enzymes to more efficiently hydrolyze the cellulose for 'deconstruction' into glucose. This process is plagued with inefficiencies, primarily due to the recalcitrance of cellulosic biomass, mass transfer issues during deconstruction, and low activity of recombinant deconstruction enzymes. Costs are also high due to the requirement for enzymes and reagents, and energy-intensive and cumbersome pretreatment steps. One potential solution to these problems is found in synthetic biology; they propose to engineer plants that self-produce a suite of cellulase enzymes targeted to the apoplast for cleaving the linkages between lignin and cellulosic fibers; the genes encoding the degradation enzymes, also known as cellulases, are obtained from extremophilic organisms that grow at high temperatures (60-100 C) and acidic pH levels (<5). These enzymes will remain inactive during the life cycle of the plant but become active during hydrothermal pretreatment i.e., elevated temperatures. Deconstruction can be integrated into a one-step process, thereby increasing efficiency (cellulose-cellulase mass-transfer rates) and reducing costs. The proposed disruptive technologies address biomass deconstruction processes by developing transgenic plants encoding a suite of enzymes used in cellulosic deconstruction. The unique aspects of this technology are the rationally engineered, highly productive extremophilic enzymes, targeted to specific cellular locations (apoplast) and their dormancy during normal plant proliferation, which become Trojan horses during pretreatment conditions. They have been leveraging established Sandia's enzyme-engineering and imaging capabilities. Their technical approach not only targets the recalcitrance and mass-transfer problem during biomass degradation but also eliminates the costs associated with industrial-scale production of microbial enzymes added during processing.

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The combination of hyperspectral confocal fluorescence microscopy and multivariate curve resolution (MCR) provides an ideal system for improved quantitative imaging when multiple fluorophores are present. However, the presence of multiple noise sources limits the ability of MCR to accurately extract pure-component spectra when there is high spectral and/or spatial overlap between multiple fluorophores. Previously, MCR results were improved by weighting the spectral images for Poisson-distributed noise, but additional noise sources are often present. We have identified and quantified all the major noise sources in hyperspectral fluorescence images. Two primary noise sources were found: Poisson-distributed noise and detector-read noise. We present methods to quantify detector-read noise variance and to empirically determine the electron multiplying CCD (EMCCD) gain factor required to compute the Poisson noise variance. We have found that properly weighting spectral image data to account for both noise sources improved MCR accuracy. In this paper, we demonstrate three weighting schemes applied to a real hyperspectral corn leaf image and to simulated data based upon this same image. MCR applied to both real and simulated hyperspectral images weighted to compensate for the two major noise sources greatly improved the extracted pure emission spectra and their concentrations relative to MCR with either unweighted or Poisson-only weighted data. Thus, properly identifying and accounting for the major noise sources in hyperspectral images can serve to improve the MCR results. These methods are very general and can be applied to the multivariate analysis of spectral images whenever CCD or EMCCD detectors are used. Copyright © 2008 John Wiley & Sons, Ltd.

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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.

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