- Poster presentation
- Open Access
Automatic recognition and statistical quantification of spatial patterns of gene expression in zebra finch brain in response to auditory stimulation
© Iancu et al; licensee BioMed Central Ltd. 2008
- Published: 11 July 2008
- Cell Nucleus
- Unsupervised Cluster
- Auditory Stimulation
- Automatic Recognition
- Edge Detection Algorithm
We present here an automated procedure for detecting and mapping spatially neuronal cells stained by in situ hybridization. In songbird brain, the subcellular localization of song-induced activity-dependent mRNA is time-dependent . Thus it is possible to distinguish neuronal cells activated at different time points and therefore the representation of two different stimuli within the same animal. Images captured through a fluorescent microscope are analyzed off-line individually and then stitched together. Furthermore, neighboring brain slices can be stacked together, resulting in a 3D map of the spatial pattern of gene expression in any given brain structure.
Several structures of the zebra finch brain display induction of immediate-early genes (IEGs) in response to auditory stimulation, including conspecific songs, a learned behavior . This transcriptional response is thought to represent a link between neuronal activation and long-term changes in neuronal circuitry, which are considered to form the basis of memory. In the caudomedial nidopallium (NCM) of the canary brain, the spatial distribution of the song-inducible gene zenk has been shown to be dependent on the stimulus presented [3, 4].
We use an edge detection algorithm to detect potential borders of cell nuclei. The Hough transform is then used to detect nearly circular shapes. False-positive shapes are separated from true cell nuclei using an unsupervised clustering algorithm. We then assign gene expression to individual cell nuclei.
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