Gene expression detection and expression visualization in in situ hybridized cross-sectional images of the mouse brain

Understanding gene expression in the mouse brain should provide a better understanding of the underlying topology of the mammalian brain, thereby opening previously unexplored avenues in neuroscience and brain informatics. An important step in this direction is to develop robust algorithms to quantify gene expression in in-situ hybridization (ISH) data. ISH methodology involves the use of labeled nucleic acid probes that bind to specific mRNA transcripts in tissue sections. The bound probe is detected using colorimetric methods and the resulting stained tissue sections are imaged at high resolution. The goals of the present study are to first identify a staining method that produces maximum signal to noise ratio (SNR), and second to develop a method for gene expression detection for a wide range of ISH images spanning different intensity and expression patterns. A simple k-means based clustering method is used to separate foreground labeling from background and non-expressing tissues in a variety of images stained with different staining/counter staining techniques. We found NBT/BCIP with no counterstain produces the best signal to background separation. The foreground cluster detected using the k-means algorithm was further modeled using a normal distribution. A novel one sided Mahalanobis distance based metric with majority partial ordered voting method was then developed to generate a fuzzy segmentation of the gene expression in each ISH image. This algorithm is fully automatic and facilitates high throughput analysis of large amount of image data. Using this methodology a cluster of 10 PCs was able to process approximately 10% of the mouse genome (17 TBytes of JPEG2000 lossless compressed images) over a period of 2 weeks. The results may be visualized at the Allen Institute for Brain Science web site www.brain-map.org.