nach oben

Neuroinformatics

Erschienen in:

12.06.2017 | Original Article

Transcriptome Architecture of Adult Mouse Brain Revealed by Sparse Coding of Genome-Wide In Situ Hybridization Images

verfasst von: Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Meng Li, Hanchuan Peng, Joe Z. Tsien, Tianming Liu

Erschienen in: Neuroinformatics | Ausgabe 3/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Highly differentiated brain structures with distinctly different phenotypes are closely correlated with the unique combination of gene expression patterns. Using a genome-wide in situ hybridization image dataset released by Allen Mouse Brain Atlas, we present a data-driven method of dictionary learning and sparse coding. Our results show that sparse coding can elucidate patterns of transcriptome organization of mouse brain. A collection of components obtained from sparse coding display robust region-specific molecular signatures corresponding to the canonical neuroanatomical subdivisions including fiber tracts and ventricular systems. Other components revealed finer anatomical delineation of domains previously considered homogeneous. We also build an open-access informatics portal that contains the detail of each component along with its ontology and expressed genes. This portal allows intuitive visualization, interpretation and explorations of the transcriptome architecture of a mouse brain.

Vorheriger Artikel Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification

Nächster Artikel A Web Resource for Levodopa-Induced Dyskinesia Genetics in Parkinson’s Disease

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Belgard, T. G., Marques, A. C., Oliver, P. L., et al. (2011). A transcriptomic atlas of mouse neocortical layers. Neuron, 71, 605–616. doi:10.1016/j.neuron.2011.06.039.CrossRefPubMedPubMedCentral

Bernard, A., Lubbers, L. S., Tanis, K. Q., et al. (2012). Transcriptional architecture of the primate neocortex. Neuron, 73, 1083–1099. doi:10.1016/j.neuron.2012.03.002.Transcriptional.CrossRefPubMedPubMedCentral

Bohland, J. W., Bokil, H., Pathak, S. D., et al. (2010). Clustering of spatial gene expression patterns in the mouse brain and comparison with classical neuroanatomy. Methods, 50, 105–112. doi:10.1016/j.ymeth.2009.09.001.CrossRefPubMed

Cahoy, J., Emery, B., Kaushal, A., et al. (2004). A transcriptome database for astrocytes, neurons, and oligodendrocytes: A new resource for understanding brain development and function. Journal of Neuro-Oncology, 28, 264–278. doi:10.1523/JNEUROSCI.4178-07.2008.

Chen, H., Liu, T., Zhao, Y., et al. (2015). Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data. NeuroImage, 115, 202–213. doi:10.1016/j.neuroimage.2015.04.050.CrossRefPubMedPubMedCentral

Comon, P. (1994). Independent component analysis: A new concept. IEEE Trans Signal Process, 36, 287–314. doi:10.1016/0165-1684(94)90029-9.

Dong, H. (2008). Allen reference atlas. A digital color brain atlas of the C57BL/6J male mouse - by H. W. Dong. John Wiley & Sons.

Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15, 3736–3745. doi:10.1109/ICIG.2009.101.CrossRefPubMed

Hawrylycz, M., Bernard, A., Lau, C., et al. (2010). Areal and laminar differentiation in the mouse neocortex using large scale gene expression data. Methods, 50, 113–121. doi:10.1016/j.ymeth.2009.09.005.CrossRefPubMed

Heintz, N. (2004). Gene expression nervous system atlas (GENSAT). Nature Neuroscience, 7, 483. doi:10.1038/nn0504-483.CrossRefPubMed

Hyvärinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10, 626–634. doi:10.1109/72.761722.CrossRefPubMed

Ishizuka, N., Weber, J., & Amaral, D. (1990). Organization of intrahippocampal projections originating from CA3 pyramidal cells in the rat. The Journal of Comparative Neurology, 295, 580–623.CrossRefPubMed

Jiang, C. H., Tsien, J. Z., Schultz, P. G., & Hu, Y. (2001). The effects of aging on gene expression in the hypothalamus and cortex of mice. PNAS, 98, 1930–1934. doi:10.1073/pnas.98.4.1930.CrossRefPubMedPubMedCentral

Lein, E. S., Hawrylycz, M. J., Ao, N., et al. (2007). Genome-wide atlas of gene expression in the adult mouse brain. Nature, 445, 168–176. doi:10.1038/nature05453.CrossRefPubMed

Lein, E. S., Zhao, X., & Gage, F. H. (2004). Defining a molecular atlas of the hippocampus using DNA microarrays and high-throughput in situ hybridization. The Journal of Neuroscience, 24, 3879–3889. doi:10.1523/JNEUROSCI.4710-03.2004.CrossRefPubMed

Lv, J., Jiang, X., Li, X., et al. (2015). Sparse representation of whole-brain fMRI signals for identification of functional networks. Medical Image Analysis, 20, 112–134. doi:10.1016/j.media.2014.10.011.CrossRefPubMed

Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2010). Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11, 19–60.

Mairal, J., Elad, M., & Sapiro, G. (2008). Sparse representation for color image restoration. IEEE Transactions on Image Processing, 17, 53–69. doi:10.1109/TIP.2007.911828.CrossRefPubMed

Mody, M., Cao, Y., Cui, Z., et al. (2001). Genome-wide gene expression profiles of the developing mouse hippocampus. PNAS, 98, 8862–8867. doi:10.1073/pnas.141244998.CrossRefPubMedPubMedCentral

Molyneaux, B. J., Arlotta, P., Menezes, J. R. L., & Macklis, J. D. (2007). Neuronal subtype specification in the cerebral cortex. Nature Reviews. Neuroscience, 8, 427–437. doi:10.1038/nrn2151.CrossRefPubMed

Mortazavi, A., Williams, B. A., McCue, K., et al. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods, 5, 621–628. doi:10.1038/nmeth.1226.CrossRefPubMed

Nelson, S. B., Sugino, K., & Hempel, C. M. (2006). The problem of neuronal cell types: a physiological genomics approach. Trends in Neurosciences, 29, 339–345. doi:10.1016/j.tins.2006.05.004.CrossRefPubMed

Ng, L., Bernard, A., Lau, C., et al. (2009). An anatomic gene expression atlas of the adult mouse brain. Nature Neuroscience, 12, 356–362. doi:10.1038/nn.2281.CrossRefPubMed

Thompson, C. L., Pathak, S. D., Jeromin, A., et al. (2008). Genomic anatomy of the hippocampus. Neuron, 60, 1010–1021. doi:10.1016/j.neuron.2008.12.008.CrossRefPubMed

Tole, S., Christian, C., & Grove, E. A. (1997). Early specification and autonomous development of cortical fields in the mouse hippocampus. Development, 124, 4959–4970.PubMed

Tsien, J., Li, M., Osan, R., et al. (2013). On initial brain activity mapping of episodic and semantic memory code in the hippocampus. Neurobiology of Learning and Memory, 105, 200–210. doi:10.1016/j.nlm.2013.06.019.On.CrossRefPubMedPubMedCentral

Winden, K. D., Oldham, M. C., Mirnics, K., et al. (2009). The organization of the transcriptional network in specific neuronal classes. Molecular Systems Biology, 5, 1–18. doi:10.1038/msb.2009.46.CrossRef

Woodhams, P., Celio, M., Ulfig, N., & Witter, M. (1993). Morphological and functional correlates of borders in the entorhinal cortex and hippocampus. Hippocampus, 3, 303–312. doi:10.1002/hipo.1993.4500030733.PubMed

Zeisel, A., Manchado, A.B.M., Codeluppi, S., et al. (2015). Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 347(80- ), 1138–42. doi: 10.1126/science.aaa1934.

Zhao, X., Lein, E. S., He, A., et al. (2001). Transcriptional profiling reveals strict boundaries between hippocampal subregions. The Journal of Comparative Neurology, 441, 187–196. doi:10.1002/cne.1406.CrossRefPubMed

Titel: Transcriptome Architecture of Adult Mouse Brain Revealed by Sparse Coding of Genome-Wide In Situ Hybridization Images
verfasst von: Yujie Li
Hanbo Chen
Xi Jiang
Xiang Li
Jinglei Lv
Meng Li
Hanchuan Peng
Joe Z. Tsien
Tianming Liu
Publikationsdatum: 12.06.2017
Verlag: Springer US
Erschienen in: Neuroinformatics / Ausgabe 3/2017
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-017-9333-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2017

Generating Neuron Geometries for Detailed Three-Dimensional Simulations Using AnaMorph

A Web Resource for Levodopa-Induced Dyskinesia Genetics in Parkinson’s Disease

Mobile Monitoring of Traumatic Brain Injury in Older Adults: Challenges and Opportunities

Improved Automatic Segmentation of White Matter Hyperintensities in MRI Based on Multilevel Lesion Features

Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification