Parallelized analysis of spatial gene expression patterns by database integration

We developed a computational framework for automated integration of a large number of two-dimensional (2D) images with three-dimensional (3D) image datasets located in the standard 3D coordinate. We applied the framework to 2,810 para-sagittal sectioned mouse brain 2D images of in situ hybridization (ISH), archived in the BrainTx database (http://www.cdtdb.neuroinf.jp). We registered the ISH images into the mouse standard coordinate space for MR images, Waxholm space (WHS, https://www.nitrc.org/projects/incfwhsmouse) by linearly transforming them into each of a series of para-sagittal MR image slices, and identifying the best-fit slice by calculating the similarity metric value (δ). Transformed 2D images were compared with 3D gene expression image datasets, which were made using a microtomy-based microarray assay system, Transcriptome Tomography, and archived in the ViBrism DB (http://vibrism.neuroinf.jp): the 3D images are located in the WHS. We first transformed ISH images of 10 regionally expressed genes and compared them to signals of corresponding 3D expression images in ViBrism DB for evaluating the integration schema: two types of data, produced with different modalities and originally located in different dimensions, were successfully compared after enhancing ISH signals against background noise. Then, for the massive transformation of BrainTx database images, we parallelized our framework, using the IPython cluster package, and implemented it on the PC cluster provided for the Brain Atlasing Hackathon activity hosted by Neuroinformatics Japan Center in Japan. We could identify the best-fit positions for all of the ISH images. All programs were made available through the GitHub repository, at the web site of neuroinformatics/bah2016_registration (https://github.com/neuroinformatics/bah2016_registration).

[1]  Allan R. Jones,et al.  Genome-wide atlas of gene expression in the adult mouse brain , 2007, Nature.

[2]  J Mazziotta,et al.  A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[3]  Gang Song,et al.  Transcriptome Tomography for Brain Analysis in the Web-Accessible Anatomical Space , 2012, PloS one.

[4]  Arthur W. Toga,et al.  MBAT: A scalable informatics system for unifying digital atlasing workflows , 2010, BMC Bioinformatics.

[5]  Toshio Kojima,et al.  Cerebellar development transcriptome database (CDT-DB): Profiling of spatio-temporal gene expression during the postnatal development of mouse cerebellum , 2008, Neural Networks.

[6]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[7]  G. Allan Johnson,et al.  Digital Atlasing and Standardization in the Mouse Brain , 2011, PLoS Comput. Biol..

[8]  Hideo Yokota,et al.  Broad Integration of Expression Maps and Co-Expression Networks Compassing Novel Gene Functions in the Brain , 2014, Scientific Reports.

[9]  G. Allan Johnson,et al.  Waxholm Space: An image-based reference for coordinating mouse brain research , 2010, NeuroImage.

[10]  P. Emson.  In-situ hybridization as a methodological tool for the neuroscientist , 1993, Trends in Neurosciences.

[11]  Trygve B. Leergaard,et al.  Waxholm Space atlas of the Sprague Dawley rat brain , 2014, NeuroImage.

[12]  Stefan Klein,et al.  Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease , 2013, Front. Neuroinform..

[13]  Y. Shinoda,et al.  Systematizing and Cloning of Genes Involved in the Cerebellar Cortex Circuit Development , 2011, Neurochemical Research.