Dataset Information

Hierarchical clustering of gene expression patterns in the Eomes + lineage of excitatory neurons during early neocortical development.

ABSTRACT:

Background

Cortical neurons display dynamic patterns of gene expression during the coincident processes of differentiation and migration through the developing cerebrum. To identify genes selectively expressed by the Eomes?+?(Tbr2) lineage of excitatory cortical neurons, GFP-expressing cells from Tg(Eomes::eGFP) Gsat embryos were isolated to > 99% purity and profiled.

Results

We report the identification, validation and spatial grouping of genes selectively expressed within the Eomes + cortical excitatory neuron lineage during early cortical development. In these neurons 475 genes were expressed ? 3-fold, and 534 genes ? 3-fold, compared to the reference population of neuronal precursors. Of the up-regulated genes, 328 were represented at the Genepaint in situ hybridization database and 317 (97%) were validated as having spatial expression patterns consistent with the lineage of differentiating excitatory neurons. A novel approach for quantifying in situ hybridization patterns (QISP) across the cerebral wall was developed that allowed the hierarchical clustering of genes into putative co-regulated groups. Forty four candidate genes were identified that show spatial expression with Intermediate Precursor Cells, 49 candidate genes show spatial expression with Multipolar Neurons, while the remaining 224 genes achieved peak expression in the developing cortical plate.

Conclusions

This analysis of differentiating excitatory neurons revealed the expression patterns of 37 transcription factors, many chemotropic signaling molecules (including the Semaphorin, Netrin and Slit signaling pathways), and unexpected evidence for non-canonical neurotransmitter signaling and changes in mechanisms of glucose metabolism. Over half of the 317 identified genes are associated with neuronal disease making these findings a valuable resource for studies of neurological development and disease.

SUBMITTER: Cameron DA

PROVIDER: S-EPMC3583225 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Hierarchical clustering of gene expression patterns in the Eomes + lineage of excitatory neurons during early neocortical development.

Cameron David A DA Middleton Frank A FA Chenn Anjen A Olson Eric C EC

BMC neuroscience 20120801

<h4>Background</h4>Cortical neurons display dynamic patterns of gene expression during the coincident processes of differentiation and migration through the developing cerebrum. To identify genes selectively expressed by the Eomes + (Tbr2) lineage of excitatory cortical neurons, GFP-expressing cells from Tg(Eomes::eGFP) Gsat embryos were isolated to > 99% purity and profiled.<h4>Results</h4>We report the identification, validation and spatial grouping of genes selectively expressed within the Eo ...[more]

PMID: 22852769

Similar Datasets

Project description:BackgroundA hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the only method applied.Methodology/principal findingsWe show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data. This is demonstrated, by testing the correspondence between the results of several algorithms (TD, glocal and BU) and the correct annotations provided by experts. The correspondence was tested in multiple domains including gene expression experiments, stock trade records and functional protein families. The performance of each of the algorithms is evaluated by statistical criteria that are assigned to clusters (nodes of the hierarchy tree) based on expert-labeled data. Whereas TD algorithms perform better on global patterns, BU algorithms perform well and are advantageous when finer granularity of the data is sought. In addition, a novel TD algorithm that is based on genuine density of the data points is presented and is shown to outperform other divisive and agglomerative methods. Application of the algorithm to more than 500 protein sequences belonging to ion-channels illustrates the potential of the method for inferring overlooked functional annotations. ClustTree, a graphical Matlab toolbox for applying various hierarchical clustering algorithms and testing their quality is made available.ConclusionsAlthough currently rarely used, global approaches, in particular, TD or glocal algorithms, should be considered in the exploratory process of clustering. In general, applying unsupervised clustering methods can leverage the quality of manually-created mapping of proteins families. As demonstrated, it can also provide insights in erroneous and missed annotations.

Dataset Information

Hierarchical clustering of gene expression patterns in the Eomes + lineage of excitatory neurons during early neocortical development.

Background

Results

Conclusions

Publications

Hierarchical clustering of gene expression patterns in the Eomes + lineage of excitatory neurons during early neocortical development.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets