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

Jagalur M, Pal C, Learned-Miller E, Zoeller RT, Kulp D - BMC Bioinformatics (2007)

Bottom Line: We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations.Resulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery.Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas 1 and the Max-Planck Institute 2 using our method.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of Massachusetts Amherst, Amherst, MA-01003, USA. manju@cs.umass.edu

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.

Results: We present an end-to-end approach for processing raw in situ expression imagery and performing subsequent analysis. We use a non-linear, information theoretic based image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting expression information within an anatomical region. Our method consists of coarse registration, fine registration, and expression feature extraction steps. From this we obtain a matrix for expression characteristics with rows corresponding to genes and columns corresponding to anatomical sub-structures. We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations.

Conclusion: Resulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery. Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas 1 and the Max-Planck Institute 2 using our method. While we focus here on imagery and experiments of the mouse brain our approach should be applicable to a variety of in situ experiments.

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In situ hybridization images. A. Reference image at position 4000. Expression images for B. Abr, C. Adcy5, D. Astn1. B is one of the best quality images. Most of the images are of quality C. D is among the worst quality.
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Figure 1: In situ hybridization images. A. Reference image at position 4000. Expression images for B. Abr, C. Adcy5, D. Astn1. B is one of the best quality images. Most of the images are of quality C. D is among the worst quality.

Mentions: In recent years, genome-wide ISH experiments have started to become publicly available, including: the Berkeley ISH embryonic fruit fly (Drosophila) experiments [4], the ISH mouse embryo experiments at the Max-Planck Institute [5], projects at Harvard [6] and Baylor [7] and the extremely large scale ISH experiments of the Allen Brain Atlas [1,8], involving over 21, 000 genes and roughly three hundred 5000 × 5000 pixel images per gene for the adult mouse brain. The processing and analysis of ISH experiments, the linking of atlas based experimental archives with relevant scientific literature, and the comparison of results with existing knowledge, together have the potential for tremendous impact on the scientific community. In our experiments here we focus on the processing and analysis of ISH experiments of the adult mouse brain using data from the Allen Brain Atlas [1,8] with properties very similar to the the Max-Planck data [5]. Figure 1 shows some examples of the imagery from the Allen Brain Atlas.


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

Jagalur M, Pal C, Learned-Miller E, Zoeller RT, Kulp D - BMC Bioinformatics (2007)

In situ hybridization images. A. Reference image at position 4000. Expression images for B. Abr, C. Adcy5, D. Astn1. B is one of the best quality images. Most of the images are of quality C. D is among the worst quality.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2230506&req=5

Figure 1: In situ hybridization images. A. Reference image at position 4000. Expression images for B. Abr, C. Adcy5, D. Astn1. B is one of the best quality images. Most of the images are of quality C. D is among the worst quality.
Mentions: In recent years, genome-wide ISH experiments have started to become publicly available, including: the Berkeley ISH embryonic fruit fly (Drosophila) experiments [4], the ISH mouse embryo experiments at the Max-Planck Institute [5], projects at Harvard [6] and Baylor [7] and the extremely large scale ISH experiments of the Allen Brain Atlas [1,8], involving over 21, 000 genes and roughly three hundred 5000 × 5000 pixel images per gene for the adult mouse brain. The processing and analysis of ISH experiments, the linking of atlas based experimental archives with relevant scientific literature, and the comparison of results with existing knowledge, together have the potential for tremendous impact on the scientific community. In our experiments here we focus on the processing and analysis of ISH experiments of the adult mouse brain using data from the Allen Brain Atlas [1,8] with properties very similar to the the Max-Planck data [5]. Figure 1 shows some examples of the imagery from the Allen Brain Atlas.

Bottom Line: We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations.Resulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery.Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas 1 and the Max-Planck Institute 2 using our method.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of Massachusetts Amherst, Amherst, MA-01003, USA. manju@cs.umass.edu

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.

Results: We present an end-to-end approach for processing raw in situ expression imagery and performing subsequent analysis. We use a non-linear, information theoretic based image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting expression information within an anatomical region. Our method consists of coarse registration, fine registration, and expression feature extraction steps. From this we obtain a matrix for expression characteristics with rows corresponding to genes and columns corresponding to anatomical sub-structures. We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations.

Conclusion: Resulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery. Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas 1 and the Max-Planck Institute 2 using our method. While we focus here on imagery and experiments of the mouse brain our approach should be applicable to a variety of in situ experiments.

Show MeSH