Design, construction, characterization, and application of a hyperspectral microarray scanner

We describe the design, construction, and operation of a hyperspectral microarray scanner for functional genomic research. The hyperspectral instrument operates with spatial resolutions ranging from 3 to 30 microm and records the emission spectrum between 490 and 900 nm with a spectral resolution of 3 nm for each pixel of the microarray. This spectral information, when coupled with multivariate data analysis techniques, allows for identification and elimination of unwanted artifacts and greatly improves the accuracy of microarray experiments. Microarray results presented in this study clearly demonstrate the separation of fluorescent label emission from the spectrally overlapping emission due to the underlying glass substrate. We also demonstrate separation of the emission due to green fluorescent protein expressed by yeast cells from the spectrally overlapping autofluorescence of the yeast cells and the growth media.     

Polychromatic microarrays: simultaneous multicolor array hybridization of eight samples

High-throughput microscale platforms have transformed modern analytical investigations. Traditional microarray analyses involve a comparative approach, with two samples, a known control and an unknown sample, hybridized side-by-side and then contrasted for genetic differences. The samples are labeled with separate dyes and hybridized together, providing a differential expression pattern based on the reporter intensities. In contrast, the fiber-optic microarray platform described herein is analyzed with a microscope, thereby enabling the use of virtually any reporter, including quantum dots. The instrumentation takes advantage of the narrow emission bands characteristic of quantum dots to perform multiplexed detection of Bacillus anthracis. Advancing beyond the standard red/green microarray experiment, a panel of eight reporters were linked to eight B. anthracis samples and simultaneously analyzed in a microarray format. The ability to employ an assortment of reporters, along with the capacity to simultaneously hybridize eight samples confers an unprecedented flexibility to array-based analyses, providing a 4-fold increase in throughput over standard two-color assays.     

End-member extraction for hyperspectral image analysis

We investigate the relationship among several popular end-member extraction algorithms, including N-FINDR, the simplex growing algorithm (SGA), vertex component analysis (VCA), automatic target generation process (ATGP), and fully constrained least squares linear unmixing (FCLSLU). We analyze the fundamental equivalence in the searching criteria of the simplex volume maximization and pixel spectral signature similarity employed by these algorithms. We point out that their performance discrepancy comes mainly from the use of a dimensionality reduction process, a parallel or sequential implementation mode, or the imposition of certain constraints. Instructive recommendations in algorithm selection for practical applications are provided.     

Method for fabricating pixelated, multicolor polarizing films

Pixelated, multicolor polarizing filters-of potential use in full-color displays-were produced by what we believe to be a novel method, i.e., masked evaporation of silver and gold onto glass substrates partially covered with separated sub-micrometer-wide strips of oriented poly(tetrafluoroethylene) (PTFE), prepared by friction deposition. The evaporated metal films preferentially nucleated at the glass surface and, consequently, formed parallel arrays in between the PTFE strips. The structures thus produced feature a strong angle-dependent absorption of polarized visible light, allowing for optical switching between red and blue and between green and yellow.     

On-chip, cell-based microarray immunofluorescence assay for high-throughput analysis of target proteins

We have developed an immunofluorescence-based assay for high-throughput analysis of target proteins on a three-dimensional cellular microarray platform. This process integrates the use of three-dimensional cellular microarrays, which should better mimic the cellular microenvironment, with sensitive immunofluorescence detection and provides quantitative information on cell function. To demonstrate this assay platform, we examined the accumulation of the alpha subunit of the hypoxia-inducible factor (HIF-1alpha) after chemical stimulation of human pancreatic tumor cells encapsulated in 3D alginate spots in volumes as low as 60 nL. We also tested the effect of the known dysregulator of HIF-1alpha, 2-methoxyestradiol (2ME2), on the levels of HIF-1alpha using a dual microarray stamping technique. This chip-based in situ Western immunoassay protocol was able to provide quantitative information on cell function, namely, the cellular response to hypoxia mimicking conditions and the reduction of HIF-1alpha levels after cell treatment with 2ME2. This system is the first to enable high-content screening of cellular protein levels on a 3D human cell microarray platform.     

Resampling-based multiple testing for microarray data analysis

The burgeoning field of genomics has revived interest in multiple testing procedures by raising new methodological and computational challenges. For example, microarray experiments generate large multiplicity problems in which thousands of hypotheses are tested simultaneously. Westfall and Young (1993) propose resampling-basedp-value adjustment procedures which are highly relevant to microarray experiments. This article discusses different criteria for error control in resampling-based multiple testing, including (a) the family wise error rate of West-fall and Young (1993) and (b) the false discovery rate developed by Benjamini and Hochberg (1995), both from a frequentist viewpoint; and (c) the positive false discovery rate of Storey (2002a), which has a Bayesian motivation. We also introduce our recently developed fast algorithm for implementing the minP adjustment to control family-wise error rate. Adjustedp-values for different approaches are applied to gene expression data from two recently published microarray studies. The properties of these procedures for multiple testing are compared.     

Autonomous system for Web-based microarray image analysis

Software-based feature extraction from DNA microarray images still requires human intervention on various levels. Manual adjustment of grid and metagrid parameters, precise alignment of superimposed grid templates and gene spots, or simply identification of large-scale artifacts have to be performed beforehand to reliably analyze DNA signals and correctly quantify their expression values. Ideally, a Web-based system with input solely confined to a single microarray image and a data table as output containing measurements for all gene spots would directly transform raw image data into abstracted gene expression tables. Sophisticated algorithms with advanced procedures for iterative correction function can overcome imminent challenges in image processing. Herein is introduced an integrated software system with a Java-based interface on the client side that allows for decentralized access and furthermore enables the scientist to instantly employ the most updated software version at any given time. This software tool is extended from PixClust as used in Extractiff incorporated with Java Web Start deployment technology. Ultimately, this setup is destined for high-throughput pipelines in genome-wide medical diagnostics labs or microarray core facilities aimed at providing fully automated service to its users.     

An introduction to DNA microarrays for gene expression analysis

This tutorial presents a basic introduction to DNA microarrays as employed for gene expression analysis, approaching the subject from a chemometrics perspective. The emphasis is on describing the nature of the measurement process, from the platforms used to a few of the standard higher-level data analysis tools employed. Topics include experimental design, detection, image processing, measurement errors, ratio calculation, background correction, normalization, and higher-level data processing. The objective is to present the chemometrician with as clear a picture as possible of an evolving technology so that the strengths and limitations of DNA microarrays are appreciated. Although the focus is primarily on spotted, two-color microarrays, a significant discussion of single-channel, lithographic arrays is also included.     

Using independent component analysis for material estimation in hyperspectral images

We develop a method for automated material estimation in hyperspectral images. The method models a hyperspectral pixel as a linear mixture of unknown materials. The method is particularly useful for applications in which material regions in a scene are smaller than one pixel. In contrast to many material estimation methods, the new method uses the statistics of large numbers of pixels rather than attempting to identify a small number of the purest pixels. The method is based on maximizing the independence of material abundances at each pixel. We show how independent component analysis algorithms can be adapted for use with this problem. We demonstrate properties of the method by application to airborne hyperspectral data.     

Secondary electron hyperspectral imaging: nanostructure and chemical analysis for the LV-SEM

ABSTRACT

Materials engineers have increasing control over nanoscale chemical composition and nanostructures. The latter can be easily analysed by the scanning electron microscope (SEM) yet nanoscale chemical analysis in the SEM poses challenges. Nevertheless, nanoscale chemical analysis capabilities have been developed in the LV-SEM by secondary electron hyperspectral imaging (SEHI). In this article, the insights that SEHI has provided into well characterised semiconductor, photovoltaic, polymer and hard carbon materials are reviewed. Instrument and experimental considerations for the obtainment of secondary electron hyper-spectra are discussed and recommendations for future analyses in the LV-SEM by SEHI are made.     

Comparative analysis of genomic signal processing for microarray data clustering

Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.     

Development of a sensitive microarray immunoassay for the quantitative analysis of neuropeptide y

A direct competitive immunoassay in an antibody microarray format was developed for the sensitive detection of neuropeptide Y (NPY) and employed in the analysis of NPY in human sweat samples. This is the first demonstration that antibody microarray, as a powerful multiplex analysis tool, can be used for the sensitive determination of NPY and potentially other neuropeptides. 400 pg/mL of dibiotinylated NPY and 0.1 mg/mL spotting capture antibody were found to offer the best performance, yielding a sensitivity of 50 pg/mL and a linear dynamic range of 0.1-100 ng/mL for NPY. Evaluation of matrix effects by using artificial sweat revealed that dialysis is necessary for analyzing NPY in human sweat samples with microarray immunoassay. In a preliminary application, 50-210 pg/mL of NPY was detected in sweat samples collected with Macroduct collectors. This study indicates that antibody microarrays can be used for NPY analysis and that human sweat could be a valuable sample source for biomarker and proteomics studies, especially when noninvasive human sample collection is preferable.     

Dynamic, electronically switchable surfaces for membrane protein microarrays

Microarray technology is a powerful tool that provides a high throughput of bioanalytical information within a single experiment. These miniaturized and parallelized binding assays are highly sensitive and have found widespread popularity especially during the genomic era. However, as drug diagnostics studies are often targeted at membrane proteins, the current arraying technologies are ill-equipped to handle the fragile nature of the protein molecules. In addition, to understand the complex structure and functions of proteins, different strategies to immobilize the probe molecules selectively onto a platform for protein microarray are required. We propose a novel approach to create a (membrane) protein microarray by using an indium tin oxide (ITO) microelectrode array with an electronic multiplexing capability. A polycationic, protein- and vesicle-resistant copolymer, poly(l-lysine)-grafted-poly(ethylene glycol) (PLL-g-PEG), is exposed to and adsorbed uniformly onto the microelectrode array, as a passivating adlayer. An electronic stimulation is then applied onto the individual ITO microelectrodes resulting in the localized release of the polymer thus revealing a bare ITO surface. Different polymer and biological moieties are specifically immobilized onto the activated ITO microelectrodes while the other regions remain protein-resistant as they are unaffected by the induced electrical potential. The desorption process of the PLL-g-PEG is observed to be highly selective, rapid, and reversible without compromising on the integrity and performance of the conductive ITO microelectrodes. As such, we have successfully created a stable and heterogeneous microarray of biomolecules by using selective electronic addressing on ITO microelectrodes. Both pharmaceutical diagnostics and biomedical technology are expected to benefit directly from this unique method.     

Design and fabrication of stacked, computer generated holograms for multicolor image generation

We present a diffractive optical element consisting of computer-generated holograms and dielectric multilayer mirrors in a stratified setup. Illuminated with a white laser beam, consisting of three single lasers with wavelengths of 635 nm, 543 nm, and 473 nm, this element enables the far field projection of arbitrary, multicolor images. Certain advantages of holographic image generation, e.g., the possibility of a large depth of focus and a very easy optical setup, are maintained with the new element.     

Radial capillary array electrophoresis microplate and scanner for high-performance nucleic acid analysis

The design, fabrication, and operation of a radial capillary array electrophoresis microplate and scanner for high-throughput DNA analysis is presented. The microplate consists of a central common anode reservoir coupled to 96 separate microfabricated separation channels connected to sample injectors on the perimeter of the 10-cm-diameter wafer. Detection is accomplished by a laser-excited rotary confocal scanner with four color detection channels. Loading of 96 samples in parallel is achieved using a pressurized capillary array system. High-quality separations of 96 pBR322 restriction digest samples are achieved in < 120 s with the microplate system. The practical utility and multicolor detection capability is demonstrated by analyzing 96 methylenetetrahydrofolate reductase (MTHFR) alleles in parallel using a noncovalent 2-color staining method. This work establishes the feasibility of performing high-throughput genotyping separations with capillary array electrophoresis microplates.     

Reliability evaluation of automated analysis, 2D scanner,and micro-tomography methods for measuring fiber dimensions in polymer-lignocellulosic fiber composites

Composite processing strongly affects the size of lignocellulosic fibers, and consequently the mechanical properties of the final product. Using a reliable method for the analysis of fiber length and diameter distributions is thus crucial for the understanding of fiber behavior during processing. In this study, three different techniques, X-ray microtomography, 2D scanning and automated fiber analyzer, were compared in terms of their reliability for the characterization of dimensions of two kinds of lignocellulosic fibers, hemp and miscanthus, in polymer-natural fiber composites. Statistical analysis was employed to interpret fiber size distributions. The study confirmed that interpreting the dimensions of natural fiber is still a difficult task. The inherent limitations of the measuring methods make each technique complementary to the others in terms of length scale. The choice of the technique is, therefore, strictly dependent on fiber dimensions and the aim of the work.     

Model for the interpretation of hyperspectral remote-sensing reflectance

Remote-sensing reflectance is easier to interpret for the open ocean than for coastal regions because the optical signals are highly coupled to the phytoplankton (e.g., chlorophyll) concentrations. For estuarine or coastal waters, variable terrigenous colored dissolved organic matter (CDOM), suspended sediments, and bottom reflectance, all factors that do not covary with the pigment concentration, confound data interpretation. In this research, remote-sensing reflectance models are suggested for coastal waters, to which contributions that are due to bottom reflectance, CDOM fluorescence, and water Raman scattering are included. Through the use of two parameters to model the combination of the backscattering coefficient and the Q factor, excellent agreement was achieved between the measured and modeled remote-sensing reflectance for waters from the West Florida Shelf to the Mississippi River plume. These waters cover a range of chlorophyll of 0.2-40 mg/m(3) and gelbstoff absorption at 440 nm from 0.02-0.4 m(-1). Data with a spectral resolution of 10 nm or better, which is consistent with that provided by the airborne visible and infrared imaging spectrometer (AVIRIS) and spacecraft spectrometers, were used in the model evaluation.     

基于高光谱成像技术的中医舌纹分析算法

针对中医舌诊现代化中使用彩色或灰度图像进行舌纹分析困难的问题,采用高光谱成像系统代替数码相机进行舌图像采集,并提出基于Gabor滤波器的舌纹分析算法.这一算法充分利用高光谱舌图像图、谱两方面的信息进行舌纹分析,并根据特征向量之间归一化的距离对部分典型舌纹进行初步分类.在对474例具有典型舌纹的高光谱舌图像的分类中,除了在来蛇纹与去蛇纹、四直纹与曲虫纹、太阳纹和龟纹、左右撇纹和锯齿纹之间有一定的误分外,整体具有较好的分类精度.这些分类结果表明,基于高光谱图像的舌纹分析方法明显优于基于灰度图像的舌纹分析方法.