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16 Publications

Showing 1-10 of 16 results
12/18/16 | Canonical genetic signatures of the adult human brain.
Hawrylycz M, Miller JA, Menon V, Feng D, Dolbeare T, Guillozet-Bongaarts AL, Jegga AG, Aronow BJ, Lee C, Bernard A, Glasser MF, Dierker DL, Menche J, Szafer A, Collman F, Grange P, Berman KA, Mihalas S, Yao Z, Stewart L, Barabási A, Schulkin J, Phillips J, Ng L, Dang C, Haynor DR, Jones A, Van Essen DC, Koch C, Lein E
Nature neuroscience. 2015 Dec;18(12):1832-44. doi: 10.1038/nn.4171

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.

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02/19/16 | Adult mouse cortical cell taxonomy revealed by single cell transcriptomics.
Tasic B, Menon V, Nguyen TN, Kim TK, Jarsky T, Yao Z, Levi B, Gray LT, Sorensen SA, Dolbeare T, Bertagnolli D, Goldy J, Shapovalova N, Parry S, Lee C, Smith K, Bernard A, Madisen L, Sunkin SM, Hawrylycz M, Koch C, Zeng H
Nature neuroscience. 2016 Feb;19(2):335-46. doi: 10.1038/nn.4216

Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type-specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.

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05/26/15 | Genome engineering of isogenic human ES cells to model autism disorders.
Martinez RA, Stein JL, Krostag AF, Nelson AM, Marken JS, Menon V, May RC, Yao Z, Kaykas A, Geschwind DH, Grimley JS
Nucleic acids research. 2015 May 26;43(10):e65. doi: 10.1093/nar/gkv164

Isogenic pluripotent stem cells are critical tools for studying human neurological diseases by allowing one to study the effects of a mutation in a fixed genetic background. Of particular interest are the spectrum of autism disorders, some of which are monogenic such as Timothy syndrome (TS); others are multigenic such as the microdeletion and microduplication syndromes of the 16p11.2 chromosomal locus. Here, we report engineered human embryonic stem cell (hESC) lines for modeling these two disorders using locus-specific endonucleases to increase the efficiency of homology-directed repair (HDR). We developed a system to: (1) computationally identify unique transcription activator-like effector nuclease (TALEN) binding sites in the genome using a new software program, TALENSeek, (2) assemble the TALEN genes by combining golden gate cloning with modified constructs from the FLASH protocol, and (3) test the TALEN pairs in an amplification-based HDR assay that is more sensitive than the typical non-homologous end joining assay. We applied these methods to identify, construct, and test TALENs that were used with HDR donors in hESCs to generate an isogenic TS cell line in a scarless manner and to model the 16p11.2 copy number disorder without modifying genomic loci with high sequence similarity.

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02/25/15 | Correlated gene expression and target specificity demonstrate excitatory projection neuron diversity.
Sorensen SA, Bernard A, Menon V, Royall JJ, Glattfelder KJ, Desta T, Hirokawa K, Mortrud M, Miller JA, Zeng H, Hohmann JG, Jones AR, Lein ES
Cerebral Cortex (New York, N.Y. : 1991). 2015 Feb;25(2):433-49. doi: 10.1093/cercor/bht243

The neocortex contains diverse populations of excitatory neurons segregated by layer and further definable by their specific cortical and subcortical projection targets. The current study describes a systematic approach to identify molecular correlates of specific projection neuron classes in mouse primary somatosensory cortex (S1), using a combination of in situ hybridization (ISH) data mining, marker gene colocalization, and combined retrograde labeling with ISH for layer-specific marker genes. First, we identified a large set of genes with specificity for each cortical layer, and that display heterogeneous patterns within those layers. Using these genes as markers, we find extensive evidence for the covariation of gene expression and projection target specificity in layer 2/3, 5, and 6, with individual genes labeling neurons projecting to specific subsets of target structures. The combination of gene expression and target specificity imply a great diversity of projection neuron classes that is similar to or greater than that of GABAergic interneurons. The covariance of these 2 phenotypic modalities suggests that these classes are both discrete and genetically specified.

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07/16/14 | A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain.
Thompson CL, Ng L, Menon V, Martinez S, Lee C, Glattfelder K, Sunkin SM, Henry A, Lau C, Dang C, Garcia-Lopez R, Martinez-Ferre A, Pombero A, Rubenstein JL, Wakeman WB, Hohmann J, Dee N, Sodt AJ, Young R, Smith K, Nguyen T, Kidney J, Kuan L, Jeromin A, Kaykas A, Miller J, Page D, Orta G, Bernard A, Riley Z, Smith S, Wohnoutka P, Hawrylycz MJ, Puelles L, Jones AR
Neuron. 2014 Jul 16;83(2):309-23. doi: 10.1016/j.neuron.2014.05.033

To provide a temporal framework for the genoarchitecture of brain development, we generated in situ hybridization data for embryonic and postnatal mouse brain at seven developmental stages for ∼2,100 genes, which were processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, seven reference atlases, an ontogenetic ontology, and tools to explore coexpression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (

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06/04/14 | Modeling proteins using a super-secondary structure library and NMR chemical shift information.
Menon V, Vallat BK, Dybas JM, Fiser A
Structure (London, England : 1993). 2013 Jun 4;21(6):891-9. doi: 10.1016/j.str.2013.04.012

A remaining challenge in protein modeling is to predict structures for sequences with no sequence similarity to any experimentally solved structure. Based on earlier observations, the library of protein backbone supersecondary structure motifs (Smotifs) saturated about a decade ago. Therefore, it should be possible to build any structure from a combination of existing Smotifs with the help of limited experimental data that are sufficient to relate the backbone conformations of Smotifs between target proteins and known structures. Here, we present a hybrid modeling algorithm that relies on an exhaustive Smotif library and on nuclear magnetic resonance chemical shift patterns without any input of primary sequence information. In a test of 102 proteins, the algorithm delivered 90 homology-model-quality models, among them 24 high-quality ones, and a topologically correct solution for almost all cases. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.

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02/24/14 | Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq.
Miller JA, Menon V, Goldy J, Kaykas A, Lee C, Smith KA, Shen EH, Phillips JW, Lein ES, Hawrylycz MJ
BMC genomics. 2014;15:154. doi: 10.1186/1471-2164-15-154

BACKGROUND: High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking.

RESULTS: To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform.

CONCLUSIONS: Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories.

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12/18/13 | Balanced synaptic impact via distance-dependent synapse distribution and complementary expression of AMPARs and NMDARs in hippocampal dendrites.
Menon V, Musial TF, Liu A, Katz Y, Kath WL, Spruston N, Nicholson DA
Neuron. 2013 Dec 18;80:1451-63. doi: 10.1016/j.neuron.2013.09.027

Neuronal computation involves the integration of synaptic inputs that are often distributed over expansive dendritic trees, suggesting the need for compensatory mechanisms that enable spatially disparate synapses to influence neuronal output. In hippocampal CA1 pyramidal neurons, such mechanisms have indeed been reported, which normalize either the ability of distributed synapses to drive action potential initiation in the axon or their ability to drive dendritic spiking locally. Here we report that these mechanisms can coexist, through an elegant combination of distance-dependent regulation of synapse number and synaptic expression of AMPA and NMDA receptors. Together, these complementary gradients allow individual dendrites in both the apical and basal dendritic trees of hippocampal neurons to operate as facile computational subunits capable of supporting both global integration in the soma/axon and local integration in the dendrite.

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10/11/13 | Dynamic Bayesian clustering.
Fowler A, Menon V, Heard NA
Journal of bioinformatics and computational biology. 2013 Oct;11(5):1342001. doi: 10.1142/S0219720013420018

Clusters of time series data may change location and memberships over time; in gene expression data, this occurs as groups of genes or samples respond differently to stimuli or experimental conditions at different times. In order to uncover this underlying temporal structure, we consider dynamic clusters with time-dependent parameters which split and merge over time, enabling cluster memberships to change. These interesting time-dependent structures are useful in understanding the development of organisms or complex organs, and could not be identified using traditional clustering methods. In cell cycle data, these time-dependent structure may provide links between genes and stages of the cell cycle, whilst in developmental data sets they may highlight key developmental transitions.

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10/09/13 | The influence of synaptic weight distribution on neuronal population dynamics.
Iyer R, Menon V, Buice M, Koch C, Mihalas S
PLoS computational biology. 2013 Oct;9(10):e1003248. doi: 10.1371/journal.pcbi.1003248

The manner in which different distributions of synaptic weights onto cortical neurons shape their spiking activity remains open. To characterize a homogeneous neuronal population, we use the master equation for generalized leaky integrate-and-fire neurons with shot-noise synapses. We develop fast semi-analytic numerical methods to solve this equation for either current or conductance synapses, with and without synaptic depression. We show that its solutions match simulations of equivalent neuronal networks better than those of the Fokker-Planck equation and we compute bounds on the network response to non-instantaneous synapses. We apply these methods to study different synaptic weight distributions in feed-forward networks. We characterize the synaptic amplitude distributions using a set of measures, called tail weight numbers, designed to quantify the preponderance of very strong synapses. Even if synaptic amplitude distributions are equated for both the total current and average synaptic weight, distributions with sparse but strong synapses produce higher responses for small inputs, leading to a larger operating range. Furthermore, despite their small number, such synapses enable the network to respond faster and with more stability in the face of external fluctuations.

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