Global analysis of gene expression and projection target correlations in the mouse brain

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Abstract

Recent studies have shown that projection targets in the mouse neocortex are correlated with their gene expression patterns. However, a brain-wide quantitative analysis of the relationship between voxel genetic composition and their projection targets is lacking to date. Here we extended those studies to perform a global, integrative analysis of gene expression and projection target correlations in the mouse brain. By using the Allen Brain Atlas data, we analyzed the relationship between gene expression and projection targets. We first visualized and clustered the two data sets separately and showed that they both exhibit strong spatial autocorrelation. Building upon this initial analysis, we conducted an integrative correlation analysis of the two data sets while correcting for their spatial autocorrelation. This resulted in a correlation of 0.19 with significant p value. We further identified the top genes responsible for this correlation using two greedy gene ranking techniques. Using only the top genes identified by those techniques, we recomputed the correlation between these two data sets. This led to correlation values up to 0.49 with significant p values. Our results illustrated that although the target specificity of neurons is in fact complex and diverse, yet they are strongly affected by their genetic and molecular compositions.

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Most cited references 22

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A mesoscale connectome of the mouse brain.

Seung Wook Oh, Julie Harris, Lydia Ng … (2014)

Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.

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Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system

Susan M. Sunkin, Lydia Ng, Chris Lau … (2012)

The Allen Brain Atlas (http://www.brain-map.org) provides a unique online public resource integrating extensive gene expression data, connectivity data and neuroanatomical information with powerful search and viewing tools for the adult and developing brain in mouse, human and non-human primate. Here, we review the resources available at the Allen Brain Atlas, describing each product and data type [such as in situ hybridization (ISH) and supporting histology, microarray, RNA sequencing, reference atlases, projection mapping and magnetic resonance imaging]. In addition, standardized and unique features in the web applications are described that enable users to search and mine the various data sets. Features include both simple and sophisticated methods for gene searches, colorimetric and fluorescent ISH image viewers, graphical displays of ISH, microarray and RNA sequencing data, Brain Explorer software for 3D navigation of anatomy and gene expression, and an interactive reference atlas viewer. In addition, cross data set searches enable users to query multiple Allen Brain Atlas data sets simultaneously. All of the Allen Brain Atlas resources can be accessed through the Allen Brain Atlas data portal.

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Cortico-cortical projections in mouse visual cortex are functionally target specific.

Lindsey Glickfeld, Mark Andermann, Vincent Bonin … (2013)

Neurons in primary sensory cortex have diverse response properties, whereas higher cortical areas are specialized. Specific connectivity may be important for areal specialization, particularly in the mouse, where neighboring neurons are functionally diverse. To examine whether higher visual areas receive functionally specific input from primary visual cortex (V1), we used two-photon calcium imaging to measure responses of axons from V1 arborizing in three areas with distinct spatial and temporal frequency preferences. We found that visual preferences of presynaptic boutons in each area were distinct and matched the average preferences of recipient neurons. This specificity could not be explained by organization within V1 and instead was due to both a greater density and greater response amplitude of functionally matched boutons. Projections from a single layer (layer 5) and from secondary visual cortex were also matched to their target areas. Thus, transmission of specific information to downstream targets may be a general feature of cortico-cortical communication.

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Contributors

Ahmed Fakhry: +1-757-683-7717 , afakhry@cs.odu.edu

Bio :

Ahmed Fakhry

is a PhD student in the Computer Science Department at Old Dominion University. His main interests are in machine learning and computational biology

Tao Zeng: +1-757-683-7717 , tzeng@cs.odu.edu

Bio :

Tao Zeng

received his M.S. degree in Neuroscience from Chinese Academy of Sciences and M.S degree in Computer Science from Old Dominion University, in 2008 and 2014, respectively. He is currently pursuing the PhD degree in Computer Science at Old Dominion University. His areas of interest include neural coding and biomedical image annotation using machine learning and data mining

Hanchuan Peng: +1-206-548-8416 , hanchuanp@alleninstitute.org

Bio :

Hanchuan Peng

leads a group of computational neuroanatomy and smart imaging at the Allen Institute for Brain Science. His current research focuses on bioimage analysis and large-scale informatics, as well as computational biology. Before joining the Allen Institute, He was the head of a computational bioimage analysis lab at Howard Hughes Medical Institute, Janelia Farm Research Campus. He is also an adjunct or affiliate professor with several USA and China universities and serves on the advisory board of a few organizations. He is the inventor of a number of algorithms and software/hardware systems, including Vaa3D, BrainAligner, NeuronTracers, SmartScope, mRMR, and 3D Virtual Finger. His recent work includes developing novel and very efficient algorithms for 3D and high-dimensional image analysis and data mining, building single-neuron whole-brain level 3D digital atlases for model animals, and Vaa3D (http://vaa3d.org), which is a high-performance visualization-assisted analysis system for large 3D/multi-dimensional biological and biomedical image datasets. He built the first neuron stereotypy map of a fruit fly brain, co-developed the first single-cell-resolution 3D digital maps of C elegans, and led one of the largest studies to date on 3D brain image registration and standardization. He was also the inventor of the widely cited minimum-Redundancy Maximum-Relevance (mRMR) feature/variable selection methods in machine learning and data mining. He was a recipient of Cozzarelli Prize (2013) and DIADEM Challenge award (2010). He was the founder of the annual Bioimage Informatics conferences (http://bioimageinformatics.org). He currently also serves as the Section Editor of BMC Bioinformatics overseeing the section of imaging, bioimage analysis, and data visualization. He also serves on the editorial board of Bioinformatics, Brain Informatics, and a few other journals.

Shuiwang Ji: +1-757-683-7717 , sji@cs.odu.edu

Bio :

Shuiwang Ji

is currently an Assistant Professor of the Computer Science Department at Old Dominion University. He received the PhD degree in Computer Science from Arizona State University in 2010. His research interests include machine learning, data mining, computational biology, and brain data analytics. He received the National Science Foundation CAREER Award in 2014. Currently, he serves as an Associate Editor for BMC Bioinformatics, IEEE Transactions on Neural Networks and Learning Systems, and Neurocomputing. He also serves as a senior program committee member for the 2015 SIAM International Conference on Data Mining and the 2015 International Joint Conference on Artificial Intelligence. He has served as a technical program committee member of major conferences in machine learning (ICML, NIPS), data mining (KDD, SDM, ICDM), and bioinformatics and medical image computing (MICCAI and PSB)

Journal

Journal ID (nlm-ta): Brain Inform

Journal ID (iso-abbrev): Brain Inform

Title: Brain Informatics

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 2198-4018

ISSN (Electronic): 2198-4026

Publication date (Electronic): 20 March 2015

Publication date PMC-release: 20 March 2015

Publication date Collection: June 2015

Volume: 2

Issue: 2

Pages: 107-117

Affiliations

[1 ]Department of Computer Science, Old Dominion University, Norfolk, VA 23529 USA

[2 ]Allen Institute for Brain Science, Seattle, WA 98103 USA

Article

Publisher ID: 14

DOI: 10.1007/s40708-015-0014-2

PMC ID: 4883149

SO-VID: 7bbd2e68-14d8-450a-8872-c7d00bfde115

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

History

Date received : 27 October 2014

Date accepted : 5 March 2015

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Keywords: projection targets,gene expression patterns,visualization,clustering,correlation,feature selection

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Keywords: projection targets, gene expression patterns, visualization, clustering, correlation, feature selection

Global analysis of gene expression and projection target correlations in the mouse brain

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Most cited references 22

A mesoscale connectome of the mouse brain.

Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system

Cortico-cortical projections in mouse visual cortex are functionally target specific.

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