Mark Hilary Van Benthem

Hyperspectral Confocal Fluorescence Imaging: Exploring Alternative Multivariate Curve Resolution Approaches

Hyperspectral confocal fluorescence microscopy, when combined with multivariate curve resolution (MCR), provides a powerful new tool for improved quantitative imaging of multi-fluorophore samples. Generally, fully non-negatively constrained models are used in the constrained alternating least squares MCR analyses of hyperspectral images since real emission components are expected to have non-negative pure emission spectra and concentrations. However, in this paper, we demonstrate four separate cases in which partially constrained models are preferred over the fully constrained MCR models. These partially constrained MCR models can sometimes be preferred when system artifacts are present in the data or where small perturbations of the major emission components are present due to environmental effects or small geometric changes in the fluorescing species. Here we demonstrate that in the cases of hyperspectral images obtained from multicomponent spherical beads, autofluorescence from fixed lung epithelial cells, fluorescence of quantum dots in aqueous solutions, and images of mercurochrome-stained endosperm portions of a wild-type corn seed, these alternative, partially constrained MCR analyses provide improved interpretability of the MCR solutions. Often the system artifacts or environmental effects are more readily described as first and/or second derivatives of the main emission components in these alternative MCR solutions since they indicate spectral shifts and/or spectral broadening or narrowing of the emission bands, respectively. Thus, this paper serves to demonstrate the need to test alternative partially constrained models when analyzing hyperspectral images with MCR methods.

Multivariate curve resolution for hyperspectral image analysis: applications to microarray technology

Multivariate curve resolution (MCR) using constrained alternating least squares algorithms represents a powerful analysis capability for a quantitative analysis of hyperspectral image data. We will demonstrate the application of MCR using data from a new hyperspectral fluorescence imaging microarray scanner for monitoring gene expression in cells from thousands of genes on the array. The new scanner collects the entire fluorescence spectrum from each pixel of the scanned microarray. Application of MCR with nonnegativity and equality constraints reveals several sources of undesired fluorescence that emit in the same wavelength range as the reporter fluorphores. MCR analysis of the hyperspectral images confirms that one of the sources of fluorescence is due to contaminant fluorescence under the printed DNA spots that is spot localized. Thus, traditional background subtraction methods used with data collected from the current commercial microarray scanners will lead to errors in determining the relative expression of low-expressed genes. With the new scanner and MCR analysis, we generate relative concentration maps of the background, impurity, and fluroescent labels over the entire image. Since the concentration maps of the fluorescent labels are relativly uaffected by the presence of background and impurity emissions, the accuracy and useful dynamic range of the gene expression data are both greatly improved over those obtained by commercial microarray scanners.

In situ analysis of LiFePO4 batteries: Signal extraction by multivariate analysis

Electrochemical reaction behavior of a commercial Li-ion battery (LiFePO4-based cathode, graphite-based anode) has been measured via in-situ neutron diffraction. Multivariate analysis was successfully applied to the neutron diffraction dataset facilitating in the determination of Li bearing phases participating in the electrochemical reaction in both the anode and cathode as a function of state-of-charge (SOC). Analysis resulted in quantified phase fraction values for LiFePO4 and FePO4 cathode compounds as well as identification of staging behavior of Li6 ,L i12, Li24 and graphite phases in the anode. An additional Li-graphite phase has also been tentatively identified during electrochemical cycling as LiC48 at conditions of ~5 to 15% SOC.

Algorithms for constrained linear unmixing with application to the hyperspectral analysis of fluorophore mixtures

In this paper, we describe the use of linear unmixing algorithms to spatially and spectrally separate fluorescence emission signals from fluorophores having highly overlapping emission spectra. Hyperspectral image data for mixtures of Nile Blue and HIDC Iodide in a methanol/polymer matrix were obtained using the Information-efficient Spectral Imaging sensor (ISIS) operated in its Hadamard Transform mode. The data were analyzed with a combination of Principal Components Analysis (PCA), orthogonal rotation, and equality and non-negativity constrained least squares methods. The analysis provided estimates of the pure-component fluorescence emission spectra and the spatial distributions of the fluorophores. In addition, spatially varying interferences from the background and laser excitation were identified and separated. A major finding resulting from this work is that the pure-component spectral estimates are very insensitive to the initial estimates supplied to the alternating least squares procedures. In fact, random number starting points reliably gave solutions that were effectively equivalent to those obtained when measured pure-component spectra were used as the initial estimates. While our proximate application is evaluating the possibility of multivariate quantitation of DNA microarrays, the results of this study should be generally applicable to hyperspectral imagery typical of remote sensing spectrometers.

Antimicrobial peptide interactions with silica bead supported bilayers and E. coli: buforin II, magainin II, and arenicin†

Using the unique quantitative capabilities of hyperspectral confocal microscopy combined with multivariate curve resolution, a comparative approach was employed to gain a deeper understanding of the different types of interactions of antimicrobial peptides (AMPs) with biological membranes and cellular compartments. This approach allowed direct comparison of the dynamics and local effects of buforin II, magainin II, and arenicin with nanoporous silica bead supported bilayers and living E. coli. Correlating between experiments and comparing these responses have yielded several important discoveries for pursuing the underlying biophysics of bacteriocidal specificity and the connection between structure and function in various cellular environments. First, a novel fluorescence method for direct comparison of a model and living system is demonstrated by utilizing the membrane partitioning and environmental sensitivity of propidium iodide. Second, measurements are presented comparing the temporal dynamics and local equilibrium concentrations of the different antimicrobial agents in the membrane and internal matrix of the described systems. Finally, we discuss how the data lead to a deeper understanding of the roles of membrane penetration and permeabilization in the action of these AMPs. Copyright

TILT-A-WHIRL: A TEXTURE ANALYSIS PACKAGE FOR 3D RENDERING OF POLE FIGURES USING MATLAB

A new MATLAB-based software suite called TILT-A-WHIRL has been applied to XRD data from textured gold films electro-deposited onto nickel substrates. The software routines facilitate phase identification, texture analysis via pole figure visualization, and macrostrain determination. The use of principal component analysis with multivariate curve resolution (PCA/MCR) revealed the extraction of texture components. The unusual hardness properties of one Au film (deposited from a 30% gold depleted BDT-200 bath) were found to be dependent on the (210) out-of-plane preferred orientation of the polycrystalline gold film. The progressive nucleation of Au crystallites during electro-plating has been tied to the improved hardness properties of this film.

A statistical approach to combining multisource information in one‐class classifiers

A new method is introduced for combining information from multiple sources to support one-class classification. The contributing sources may represent measurements taken by different sensors of the same physical entity, repeated measurements by a single sensor, or numerous features computed from a single measured image or signal. The approach utilizes the theory of statistical hypothesis testing, and applies Fishers technique for combining p-values, modified to handle nonindependent sources. Classifier outputs take the form of fused p-values, which may be used to gauge the consistency of unknown entities with one or more class hypotheses. The approach enables rigorous assessment of classification uncertainties, and allows for traceability of classifier decisions back to the constituent sources, both of which are important for high-consequence decision support. Application of the technique is illustrated in two challenge problems, one for skin segmentation and the other for terrain labeling. The method is seen to be particularly effective for relatively small training samples.

Hyperspectral Imaging of Oil Producing Microalgae under Thermal and Nutritional Stress

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.

3D optical sectioning with a new hyperspectral confocal fluorescence imaging system.

A novel hyperspectral fluorescence microscope for high-resolution 3D optical sectioning of cells and other structures has been designed, constructed, and used to investigate a number of different problems. We have significantly extended new multivariate curve resolution (MCR) data analysis methods to deconvolve the hyperspectral image data and to rapidly extract quantitative 3D concentration distribution maps of all emitting species. The imaging system has many advantages over current confocal imaging systems including simultaneous monitoring of numerous highly overlapped fluorophores, immunity to autofluorescence or impurity fluorescence, enhanced sensitivity, and dramatically improved accuracy, reliability, and dynamic range. Efficient data compression in the spectral dimension has allowed personal computers to perform quantitative analysis of hyperspectral images of large size without loss of image quality. We have also developed and tested software to perform analysis of time resolved hyperspectral images using trilinear multivariate analysis methods. The new imaging system is an enabling technology for numerous applications including (1) 3D composition mapping analysis of multicomponent processes occurring during host-pathogen interactions, (2) monitoring microfluidic processes, (3) imaging of molecular motors and (4) understanding photosynthetic processes in wild type and mutant Synechocystis cyanobacteria.

Multivariate Statistical Analysis of a Multimodal Diffraction and X-ray Spectral Series Data Set

Multivariate statistical analysis (MSA) methods comprise a number of powerful techniques for reducing high-dimension data to a more manageable and interpretable lower dimension solution [1-2]. Moving beyond spectroscopic data large image and diffraction series can now be acquired [2,3]. Previously the analysis with MSA of a 183 Gb diffraction series was described [2] and in this work we will describe a deeper look in to the same data set combined with simultaneously acquired X-ray spectral data at each real-space point.