Over the past decade optical approaches were introduced that effectively break the diffraction barrier. Of particular note were introductions of Stimulated Emission/Depletion (STED) microscopy, Photo-Activated Localization Microscopy (PALM), and the closely related Stochastic Optical Reconstruction Microscopy (STORM). STORM represents an attractive method for researchers, as it does not require highly specialized optical setups, can be implemented using commercially available dyes, and is more easily amenable to multicolor imaging. We implemented a simultaneous dual-color, direct-STORM imaging system through the use of an objective-based TIRF microscope and filter-based image splitter. This system allows for excitation and detection of two fluorophors simultaneously, via projection of each fluorophor's signal onto separate regions of a detector. We imaged the sub-resolution organization of the TLR4 receptor, a key mediator of innate immune response, after challenge with lipopolysaccharide (LPS), a bacteria-specific antigen. While distinct forms of LPS have evolved among various bacteria, only some LPS variations (such as that derived from E. coli) typically result in significant cellular immune response. Others (such as from the plague bacteria Y. pestis) do not, despite affinity to TLR4. We will show that challenge with LPS antigens produces a statistically significant increase in TLR4 receptor clusters on the cell membrane, presumably due to recruitment of receptors to lipid rafts. These changes, however, are only detectable below the diffraction limit and are not evident using conventional imaging methods. Furthermore, we will compare the spatiotemporal behavior of TLR4 receptors in response to different LPS chemotypes in order to elucidate possible routes by which pathogens such as Y. pestis are able to circumvent the innate immune system. Finally, we will exploit the dual-color STORM capabilities to simultaneously image LPS and TLR4 receptors in the cellular membrane at resolutions at or below 40nm.
A considerable amount research is being conducted on microalgae, since microalgae are becoming a promising source of renewable energy. Most of this research is centered on lipid production in microalgae because microalgae produce triacylglycerol which is ideal for biodiesel fuels. Although we are interested in research to increase lipid production in algae, we are also interested in research to sustain healthy algal cultures in large scale biomass production farms or facilities. The early detection of fluctuations in algal health, productivity, and invasive predators must be developed to ensure that algae are an efficient and cost-effective source of biofuel. Therefore we are developing technologies to monitor the health of algae using spectroscopic measurements in the field. To do this, we have proposed to spectroscopically monitor large algal cultivations using LIDAR (Light Detection And Ranging) remote sensing technology. Before we can deploy this type of technology, we must first characterize the spectral bio-signatures that are related to algal health. Recently, we have adapted our confocal hyperspectral imaging microscope at Sandia to have two-photon excitation capabilities using a chameleon tunable laser. We are using this microscope to understand the spectroscopic signatures necessary to characterize microalgae at the cellular level prior to using these signatures to classify the health of bulk samples, with the eventual goal of using of LIDAR to monitor large scale ponds and raceways. By imaging algal cultures using a tunable laser to excite at several different wavelengths we will be able to select the optimal excitation/emission wavelengths needed to characterize algal cultures. To analyze the hyperspectral images generated from this two-photon microscope, we are using Multivariate Curve Resolution (MCR) algorithms to extract the spectral signatures and their associated relative intensities from the data. For this presentation, I will show our two-photon hyperspectral imaging results on a variety of microalgae species and show how these results can be used to characterize algal ponds and raceways.