Cell-type-based model explaining coexpression patterns of genes in the brain

Spatial patterns of gene expression in the vertebrate brain are not independent, as pairs of genes can exhibit complex patterns of coexpression. Two genes may be similarly expressed in one region, but differentially expressed in other regions. These correlations have been studied quantitatively, particularly for the Allen Atlas of the adult mouse brain, but their biological meaning remains obscure. We propose a simple model of the coexpression patterns in terms of spatial distributions of underlying cell types and establish its plausibility using independently measured cell-typespecific transcriptomes. The model allows us to predict the spatial distribution of cell types in the mouse brain

The Effectiveness of Outdoor Education on Environmental Learning, Appreciation, and Activism

The main objective of this research was to determine the effectiveness of outdoor education on student knowledge retention, appreciation for nature, and environmental activism in a college level course on south Florida ecology. Six class sections were given quizzes on four course topics either post-lecture or post-field trip. Students were also given pre-course and post-course opinion surveys. Although mean quiz scores for the post-field trip were higher than for the post-lecture, statistical analysis determined that there was no significant difference in quiz scores for location taken (post-lecture or post-field trip). Survey results show a correlation between knowledge of environmental issues and environmental activism. Even though student survey responses point to outdoor education and field trips being the most effective method of learning and influential on appreciation for nature, the quiz scores do not reflect such

Cortical fast-spiking parvalbumin interneurons enwrapped in the perineuronal net express the metallopeptidases Adamts8, Adamts15 and Neprilysin.

The in situ hybridization Allen Mouse Brain Atlas was mined for proteases expressed in the somatosensory cerebral cortex. Among the 480 genes coding for protease/peptidases, only four were found enriched in cortical interneurons: Reln coding for reelin; Adamts8 and Adamts15 belonging to the class of metzincin proteases involved in reshaping the perineuronal net (PNN) and Mme encoding for Neprilysin, the enzyme degrading amyloid β-peptides. The pattern of expression of metalloproteases (MPs) was analyzed by single-cell reverse transcriptase multiplex PCR after patch clamp and was compared with the expression of 10 canonical interneurons markers and 12 additional genes from the Allen Atlas. Clustering of these genes by K-means algorithm displays five distinct clusters. Among these five clusters, two fast-spiking interneuron clusters expressing the calcium-binding protein Pvalb were identified, one co-expressing Pvalb with Sst (PV-Sst) and another co-expressing Pvalb with three metallopeptidases Adamts8, Adamts15 and Mme (PV-MP). By using Wisteria floribunda agglutinin, a specific marker for PNN, PV-MP interneurons were found surrounded by PNN, whereas the ones expressing Sst, PV-Sst, were not

Getting Down to Specifics: Profiling Gene Expression and Protein-DNA Interactions in a Cell Type-Specific Manner.

The majority of multicellular organisms are comprised of an extraordinary range of cell types, with different properties and gene expression profiles. Understanding what makes each cell type unique, and how their individual characteristics are attributed, are key questions for both developmental and neurobiologists alike. The brain is an excellent example of the cellular diversity expressed in the majority of eukaryotes. The mouse brain comprises of approximately 75 million neurons varying in morphology, electrophysiology, and preferences for synaptic partners. A powerful process in beginning to pick apart the mechanisms that specify individual characteristics of the cell, as well as their fate, is to profile gene expression patterns, chromatin states, and transcriptional networks in a cell type-specific manner, i.e. only profiling the cells of interest in a particular tissue. Depending on the organism, the questions being investigated, and the material available, certain cell type-specific profiling methods are more suitable than others. This chapter reviews the approaches presently available for selecting and isolating specific cell types and evaluates their key features

Transcriptional profiling at whole population and single cell levels reveals somatosensory neuron molecular diversity

The somatosensory nervous system is critical for the organism's ability to respond to mechanical, thermal, and nociceptive stimuli. Somatosensory neurons are functionally and anatomically diverse but their molecular profiles are not well-defined. Here, we used transcriptional profiling to analyze the detailed molecular signatures of dorsal root ganglion (DRG) sensory neurons. We used two mouse reporter lines and surface IB4 labeling to purify three major non-overlapping classes of neurons: 1) IB4+SNS-Cre/TdTomato+, 2) IB4−SNS-Cre/TdTomato+, and 3) Parv-Cre/TdTomato+ cells, encompassing the majority of nociceptive, pruriceptive, and proprioceptive neurons. These neurons displayed distinct expression patterns of ion channels, transcription factors, and GPCRs. Highly parallel qRT-PCR analysis of 334 single neurons selected by membership of the three populations demonstrated further diversity, with unbiased clustering analysis identifying six distinct subgroups. These data significantly increase our knowledge of the molecular identities of known DRG populations and uncover potentially novel subsets, revealing the complexity and diversity of those neurons underlying somatosensation.This work was supported by CJW NIH R37 NS039518; R01 NS038253; 1PO1 NS072040-01; and the Dr. Miriam and Sheldon G. Adelson Medical Foundation. IMC received fellowship support from NIH F32 NS076297-01. Gene expression analysis were performed in the IDDRC Molecular Genetics Core facility at Boston Children's Hospital, supported by National Institutes of Health award NIH-P50-NS40828. Flow cytometry was performed in the IDDRC Stem Cell Core Facility at Boston Children's Hospital, supported by NIH-P30-HD18655. Microarray work was conducted at the Boston Children's Hospital IDDRC Molecular Genetics Core, supported by NIH-P30-HD 18655

Networks of Neuronal Genes Affected by Common and Rare Variants in Autism Spectrum Disorders

Autism spectrum disorders (ASD) are neurodevelopmental disorders with phenotypic and genetic heterogeneity. Recent studies have reported rare and de novo mutations in ASD, but the allelic architecture of ASD remains unclear. To assess the role of common and rare variations in ASD, we constructed a gene co-expression network based on a widespread survey of gene expression in the human brain. We identified modules associated with specific cell types and processes. By integrating known rare mutations and the results of an ASD genome-wide association study (GWAS), we identified two neuronal modules that are perturbed by both rare and common variations. These modules contain highly connected genes that are involved in synaptic and neuronal plasticity and that are expressed in areas associated with learning and memory and sensory perception. The enrichment of common risk variants was replicated in two additional samples which include both simplex and multiplex families. An analysis of the combined contribution of common variants in the neuronal modules revealed a polygenic component to the risk of ASD. The results of this study point toward contribution of minor and major perturbations in the two sub-networks of neuronal genes to ASD risk

Mixed-species RNA-seq for elucidating non-cell-autonomous control of gene transcription

Transcriptomic changes induced in one cell type by another mediate many biological processes in the brain and elsewhere; however, achieving artefact-free physical separation of cell types to study them is challenging and generally only allows for analysis of a single cell type. We describe an approach employing co-culture of distinct cell-types from different species, which enables physical cell sorting to be replaced by in silico RNA sequencing (RNA-seq) read sorting due to evolutionary divergence of mRNA sequence. As an exemplary experiment, we describe the co-culture of purified neurons, astrocytes, and microglia from different species (12–14 days). Following conventional RNA-seq, we then describe how to use our Python tool Sargasso (http://statbio.github.io/Sargasso/) to separate reads according to species and how to eliminate any artefacts borne out of imperfect genome annotation (10 hours). We show how this procedure, which requires no special skills beyond those that might normally be expected of wet-lab and bioinformatics researchers, enables the simultaneous transcriptomic profiling of different cell types, revealing the distinct influence of microglia on astrocytic and neuronal transcriptomes under inflammatory conditions