Analyzing in situ gene expression in the mouse brain with image registration, feature extraction and block clustering

Abstract

Background: Many important high throughput projects use in situ hybridization and may require the analysis of images of spatial cross sections of organisms taken with cellular level resolution. Projects creating gene expression atlases at unprecedented scales for the embryonic fruit fly as well as the embryonic and adult mouse already involve the analysis of hundreds of thousands of high resolution experimental images mapping mRNA expression patterns. Challenges include accurate registration of highly deformed tissues, associating cells with known anatomical regions, and identifying groups of genes whose expression is coordinately regulated with respect to both concentration and spatial location. Solutions to these and other challenges will lead to a richer understanding of the complex system aspects of gene regulation in heterogeneous tissue.

Cell type-specific genes show striking and distinct patterns of spatial expression in the mouse brain

“…We used all available images within a 200-μm range in the centermost part of the brain, one to three images per gene on the coronal plane and one to three images on the sagittal plane. Downloaded images were registered to a standard size and shape, following the protocol of Jagalur et al (25). We then performed bicubic interpolation to generate average expression levels for patches within a 300 × 300 grid across each image.…”

Integrating Copies Obtained from Old and New Preservation Efforts

“…We present our registration algorithm, highlighting the changes relative to [9], which will be referred to as the baseline algorithm.…”

“…We take our inspiration from the work of Jagalur et al [9], describing a registration algorithm comprising two main steps: a coarse similarity transformation step (a subset of affine transformation including translation, rotation and scaling, excluding shearing) is first applied globally to one of the images (new image) to bring it into rough alignment with the other (old image). Then, a fine local warping step is performed to handle local misalignments.…”

“…Given the old (reference) image and the new (target) image, the algorithm first performs a similarity transformation on the target image to try and roughly align it to the reference image, as in [9]. The main objective of this step is to reduce fine-step complexity.…”

“…As the algorithm in [9] was targeted to mice brain images, it required modification to be suited for registration of historical, hand-written texts. We present our registration algorithm, highlighting the changes relative to [9], which will be referred to as the baseline algorithm.…”