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Characterization of differential toll-like receptor responses below the optical diffraction limit

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Aaron, Jesse S.; Carson, Bryan; Timlin, Jerilyn A.

Many membrane receptors are recruited to specific cell surface domains to form nanoscale clusters upon ligand activation. This step appears to be necessary to initiate cell signaling, including pathways in innate immune system activation. However, virulent pathogens such as Yersinia pestis (the causative agent of plague) are known to evade innate immune detection, in contrast to similar microbes (such as Escherichia coli) that elicit a robust response. This disparity has been partly attributed to the structure of lipopolysaccharides (LPS) on the bacterial cell wall, which are recognized by the innate immune receptor TLR4. It is hypothesized that nanoscale differences exist between the spatial clustering of TLR4 upon binding of LPS derived from Y. pestis and E. coli. Although optical imaging can provide exquisite details of the spatial organization of biomolecules, there is a mismatch between the scale at which receptor clustering occurs (<300 nm) and the optical diffraction limit (>400 nm). The last decade has seen the emergence of super-resolution imaging methods that effectively break the optical diffraction barrier to yield truly nanoscale information in intact biological samples. This study reports the first visualizations of TLR4 distributions on intact cells at image resolutions of <30 nm using a novel, dual-color stochastic optical reconstruction microscopy (STORM) technique. This methodology permits distinction between receptors containing bound LPS from those without at the nanoscale. Importantly, it is also shown that LPS derived from immunostimulatory bacteria result in significantly higher LPS-TLR4 cluster sizes and a nearly twofold greater ligand/receptor colocalization as compared to immunoevading LPS. A dual-color stochastic optical reconstruction microscopy technique is employed to gain insight into the nanoscale organization of the innate immune system receptor TLR4. Data indicate significant changes in TLR4 clustering behavior within the cell membrane in response to immunostimulatory and immunoevading bacterial antigens, thereby shedding light on virulence mechanisms of highly pathogenic microbes such as Yersinia pestis. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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TYPE Journal Article YEAR 2012
DOIOSTIScopus

Preprocessing strategies to improve MCR analyses of hyperspectral images

Chemometrics and Intelligent Laboratory Systems

Jones, Howland D.T.; Sinclair, Michael B.; Melgaard, David K.; Collins, Aaron M.; Timlin, Jerilyn A.

Multivariate curve resolution (MCR) is a useful and important analysis tool for extracting quantitative information from hyperspectral image data. However, in the case of hyperspectral fluorescence microscope images acquired with CCD-type technologies, cosmic spikes and the presence of detector artifacts in the spectral data can make the extraction of the pure-component spectra and their relative concentrations challenging when applying MCR to the images. In this paper, we present new generalized and automated approaches for preprocessing spectral image data to improve the robustness of the MCR analysis of spectral images. These novel preprocessing steps remove cosmic spikes, correct for the presence of detector offsets and structured noise as well as select spectral and spatial regions to reduce the detrimental effects of detector noise. These preprocessing and MCR analysis techniques incorporate the use of an optical filter to prevent light from impinging on a small number of spectral pixels in the CCD detector. This dark spectral region can be incorporated into any spectral imaging system to enhance modeling of detector offset and structured noise components as well as the automated selection of spatial regions to restrict the analysis to only those regions containing viable spectral information. The success of these automated preprocessing methods combined with new MCR modeling approaches are demonstrated with realistically simulated data derived from spectral images of macrophage cells with green fluorescence protein (GFP). Further, we demonstrate using spectral images from the green alga, Chlorella, approaches for the analyses when fluorescent species with widely different relative spectral intensities are present in the image. We believe that the preprocessing and MCR approaches introduced in this paper can be generalized to several other hyperspectral image technologies and can improve the success of automated MCR analyses with little or no a priori information required about the spectral components present in the samples. © 2012 Elsevier B.V.

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TYPE Journal Article YEAR 2012
OSTIScopus
Results 176–200 of 277
Results 176–200 of 277