Multivariate Curve Resolution for Hyperspectral Image Analysis: Applications to Microarray Technology David M. Haaland, Jerilyn A. Timlin, Michael B. Sinclair, and Mark H. Van Benthem Sandia National Laboratories, Albuquerque, NM 87185-0886 and M. Juanita Martinez, Anthony D. Aragon, and Margaret Werner-Washburne, University of New Mexico, Albuquerque, NM 87131 ABSTRACT Multivariate curve resolution (MCR) using constrained alternating least squares algorithms represents a powerful analysis capability for the 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 fluorophores. 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 fluorescent labels over the entire image. Since the concentration maps of the fluorescent labels are relatively unaffected 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. Keywords: Hyperspectral image analysis, Multivariate Curve Resolution, MCR, Microarray analysis, Fluorescence imaging 1. INTRODUCTION Microarray technology is a relatively recent experimental development that allows high-throughput analysis of relative gene expressions of thousands of genes of an organism. The full details of the microarray process can be found in Schena. 1 In the standard microarray experiment, single-strand DNA gene fragments of known sequence are printed on glass slides in small spots on 150 to 250 µm centers. Up to 20,000 gene fragments (gene probes) can be printed on each glass slide. The microarray technology generally makes binary comparisons of gene expression from an organism for each microarray slide. The binary comparisons can be between cells in two different states or conditions such as between normal and abnormal (e.g., normal vs. cancerous cells). Messenger RNA (mRNA) is generated when a gene is being expressed in the cell, and the mRNA is subsequently extracted from the cells in the two states to be compared during the microarray experiment. The amount of mRNA is assumed to be proportional to the extent of gene expression of the cells in the two states. The mRNA from each cell type is then translated into single-strand cDNA (gene targets) and each labeled with a different fluorescent tag during the translation. The two labeled cDNA solutions are allowed to hybridize to the printed DNA attached to the microarray slide. The labeled hybridized microarray slide is scanned with one of several available commercial microarray scanners that are sensitive optical filter fluorescence imaging systems. Specialized software is used to quantify the emission signal from the fluorescent label in the spot and the background signal around the spot. Ratios of the background-corrected and normalized signals yield quantitative measures of which genes are enhanced and which are repressed in the test sample relative to the control sample. Microarray experiments have been demonstrated to be very effective for exploring the relative gene expressions of organisms under various conditions. Results from microarray experiments can be used to comprehensively and