Image Segmentation and Quality Control Measures in Microarray Image Analysis

INTRODUCTION The goal of microarray experiments is quantifying the relative abundance of mRNA species contained within two or more biological samples sharing homologies with cDNA or oligonucleotides spotted (printed) onto a solid surface such as glass slides or nylon membranes. Automating the process has become a critical issue due to the magnitude of different cDNAs or oligonucleotides that can now be positioned on a single array. A single slide may contain up to 10,000 different sequences. Automation becomes even more critical considering that contemporary experiments often involve multi-slide protocols containing replicate samples and multiple controls. The current state-of-the-art utilizes an approach where each mRNA sample is reverse transcribed in the presence of a different fluorochrome-labeled dNTP and the resulting cDNAs are applied to the microarray slide. The relative binding of the labeled cDNAs is quantified by multiple scans of the slide with a laser set at the appropriate excitation wavelengths and recording an image of the slide in the emitted wavelengths. The scans are typically reported out as images in 16-bit grayscale TIFF file format. With this approach, the intensity of emitted light from an area of the slide containing spotted cDNA or oligonucleotide will be proportional to the amount of bound, fluorochrome-labeled probe over the dynamic range of the system. Processing of the scanned images of microarray slides consists of four steps: 1) measuring the signal intensity of each of the arrayed spots, 2) assessing the reliability of the data, 3) identifying any signal anomalies which may indicate problems in array fabrication or the performance of the hybridization steps, and 4) quantifying relative transcript abundance based on these intensities. A straightforward software solution for automating the first step would be superimposing a second, virtual image consisting of a grid of circles with both the same geometry as the spacing of the spots on the slide and the same diameter as the spots over the original image, followed by quantifying the pixels falling within and outside the individual circles. The former data would be the spot signals and the latter, background. The software would then package the data in a digital file format appropriate for direct import into other software packages for further analysis and databasing. Ideally, this last step would include associating the intensity measurements with unique names identifying each of the spotted cDNA sequences. The need for the second and third step listed above stems from complications, which occur during slide fabrication and the hybridization process. These complications require more robust automation software with the capability to provide data relating to image and spot quality. In terms of potential issues introduced during array fabrication, spot position is often inconsistent due to mechanical constraints in the spotting process. Mechanical constraints may also introduce spots with irregular shapes. Additionally, some drying rates may result in uneven distribution of the spotted sample leading to irregularities on the spot surface producing specular reflections in the image. It is also the case that extraneous signals may arise from splashes and drips of the DNA solutions occurring during printing as well as physical imperfections in glass substrate. Adding to the problem are environmental artifacts such as dust which may be introduced at any time during fabrication or hybridization and typically appear as very bright images in scanned arrays. These artificially high signals may occur in areas of the slide distinctly separated from arrayed spots as well as directly over true spots. Finally, artifacts in the images can be introduced during the hybridization procedure. The occurrence of spots with signal intensities below that of background is often ascribed to flaws in the hybridization protocol. Given these considerations, a fully functional software package for microarray image analysis would also need to automatically identify contamination and inconsistent spot placement and shape as well as flag spots of poor quality.