Automated characterization of gene expression patterns with an atlas of the mouse brain

Extracellular matrix in lung development, homeostasis and disease

The lung's unique extracellular matrix (ECM), while providing structural support for cells, is critical in the regulation of developmental organogenesis, homeostasis and injury-repair responses. The ECM, via biochemical or biomechanical cues, regulates diverse cell functions, fate and phenotype. The composition and function of lung ECM become markedly deranged in pathological tissue remodeling. ECM-based therapeutics and bioengineering approaches represent promising novel strategies for regeneration/repair of the lung and treatment of chronic lung diseases. In this review, we assess the current state of lung ECM biology, including fundamental advances in ECM composition, dynamics, topography, and biomechanics; the role of the ECM in normal and aberrant lung development, adult lung diseases and autoimmunity; and ECM in the regulation of the stem cell niche. We identify opportunities to advance the field of lung ECM biology and provide a set recommendations for research priorities to advance knowledge that would inform novel approaches to the pathogenesis, diagnosis, and treatment of chronic lung diseases.

A chicken embryo cardiac outflow tract atlas for registering changes due to abnormal blood flow

Subdivision-based image registration has previously been applied to co-localize digital information extracted from rigid structures in biological specimens, such as the brain. Here, we describe and demonstrate the creation and application of a two-dimensional subdivision-based atlas representing a dynamic structure: the outflow tract of the developing chicken heart. The atlas is designed to segment three different anatomical layers of the outflow tract, and is demonstrated on the characterization of collagen XIV in both control and induced abnormal flow specimens. Abnormal blood flow in the embryonic developing heart can lead to congenital heart disease. Comparing local cellular and sub-cellular changes that are caused by abnormal flow can assist in understanding the molecular pathways involved in maladaptations of the heart and congenital defects. This study demonstrates the approach and potential for more extensive applications of the subdivision-based atlas for the embryonic chicken heart.

A digital atlas of ion channel expression patterns in the two-week-old rat brain

The approximately 350 ion channels encoded by the mammalian genome are a main pillar of the nervous system. We have determined the expression pattern of 320 channels in the two-week-old (P14) rat brain by means of non-radioactive robotic in situ hybridization. Optimized methods were developed and implemented to generate stringently coronal brain sections. The use of standardized methods permits a direct comparison of expression patterns across the entire ion channel expression pattern data set and facilitates recognizing ion channel co-expression. All expression data are made publically available at the Genepaint.org database. Inwardly rectifying potassium channels (Kir, encoded by the Kcnj genes) regulate a broad spectrum of physiological processes. Kcnj channel expression patterns generated in the present study were fitted with a deformable subdivision mesh atlas produced for the P14 rat brain. This co-registration, when combined with numerical quantification of expression strengths, allowed for semi-quantitative automated annotation of expression patterns as well as comparisons among and between Kcnj subfamilies. The expression patterns of Kcnj channel were also cross validated against previously published expression patterns of Kcnj channel genes.

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

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.

Automated pipeline for atlas-based annotation of gene expression patterns: application to postnatal day 7 mouse brain

Massive amounts of image data have been collected and continue to be generated for representing cellular gene expression throughout the mouse brain. Critical to exploiting this key effort of the post-genomic era is the ability to place these data into a common spatial reference that enables rapid interactive queries, analysis, data sharing, and visualization. In this paper, we present a set of automated protocols for generating and annotating gene expression patterns suitable for the establishment of a database. The steps include imaging tissue slices, detecting cellular gene expression levels, spatial registration with an atlas, and textual annotation. Using high-throughput in situ hybridization to generate serial sets of tissues displaying gene expression, this process was applied toward the establishment of a database representing over 200 genes in the postnatal day 7 mouse brain. These data using this protocol are now well-suited for interactive comparisons, analysis, queries, and visualization.

A digital atlas to characterize the mouse brain transcriptome

Massive amounts of data are being generated in an effort to represent for the brain the expression of all genes at cellular resolution. Critical to exploiting this effort is the ability to place these data into a common frame of reference. Here we have developed a computational method for annotating gene expression patterns in the context of a digital atlas to facilitate custom user queries and comparisons of this type of data. This procedure has been applied to 200 genes in the postnatal mouse brain. As an illustration of utility, we identify candidate genes that may be related to Parkinson disease by using the expression of a dopamine transporter in the substantia nigra as a search query pattern. In addition, we discover that transcription factor Rorb is down-regulated in the barrelless mutant relative to control mice by quantitative comparison of expression patterns in layer IV somatosensory cortex. The semi-automated annotation method developed here is applicable to a broad spectrum of complex tissues and data modalities.

The mammalian brain is a complex organ with hundreds of functional parts. Describing when and where genes are expressed in the brain is thus a potentially powerful method for understanding the function of gene products. In recent years, several mammalian genomes including those of human and mouse have been characterized. There are now efforts around the world that aim to determine the expression patterns for all genes in the mouse brain. To search these expression data readily, they must be placed into an atlas. The authors propose a new method for bringing such genetic data into a common spatial framework so that one can perform spatial searches and comparisons of gene expression patterns. To create this atlas, the authors developed a series of maps of the brain using a graphical modeling method called subdivision. These maps were deformed to match the shape of tissue sections, and genetic activity information was associated with the appropriate coordinates on the map. After placing 200 genes into the context of this atlas, the authors illustrate its application in discovering genes potentially involved in diseases and brain development.

A method for automated detection of gene expression required for the establishment of a digital transcriptome-wide gene expression atlas

Acquiring information about the expression of a gene in different cell populations and tissues can provide key insight into the function of the gene. A high-throughput in situ hybridization (ISH) method was recently developed for rapid and reproducible acquisition of gene expression patterns in serial tissue sections at cellular resolution. Characterizing and analysing expression patterns on thousands of sections requires efficient methods for locating cells and estimating the level of expression in each cell. Such cellular quantification is an essential step in both annotating and quantitatively comparing high-throughput ISH results. Here we describe a novel automated and efficient methodology for performing this quantification on postnatal mouse brain.