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Automatic recognition and statistical quantification of spatial patterns of gene expression in zebra finch brain in response to auditory stimulation


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 [2]. 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 [2]. 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.


We have automated the detection of nuclear and cytoplasmic patterns gene expression in the NCM of zebra finch (see Figure 1). The spatial distribution of activated cells was used to determine the sensory representation of different stimulus classes. This procedure reveals the relationship between the stimulus classes used to induce the gene expression and the spatial pattern of induction.

Figure 1

Example of image segmentation and automatic labeling. Left: raw image. Right: detected nuclear patterns (red dots) and cytoplasmic (red circles).


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    Velho TAF, Iancu OD, Roberts P, Mello CV: Differential auditory representation of complex sounds in the zebra finch brain.

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    Velho TAF, Mello CV: Transcriptional regulation of activity-dependent genes by birdsong. Transcriptional Regulation by Neuronal Activity. Edited by: Serena Dudek. 2007, Springer Verlag, 3-26.

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    Ribeiro S, Cecchi GA, Magnasco MO, Mello CV: Towards a song code: evidence for a syllabic representation in the canary brain. Neuron. 1998, 21: 359-371. 10.1016/S0896-6273(00)80545-0.

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Correspondence to Ovidiu D Iancu.

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Iancu, O.D., Velho, T., Roberts, P. et al. Automatic recognition and statistical quantification of spatial patterns of gene expression in zebra finch brain in response to auditory stimulation. BMC Neurosci 9, P68 (2008).

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  • Cell Nucleus
  • Unsupervised Cluster
  • Auditory Stimulation
  • Automatic Recognition
  • Edge Detection Algorithm