Target-Captured mRNA from Murine Olfactory Bulb.

The external world is perceived via sensory receptors arranged in highly organized systems according to functional strategies, which in turn reflect features of critical importance to both the sense and the animal. Thus describing the receptor organization and functional strategies of olfaction, the sense of smell, is crucial for a concrete understanding of the fundamental principles of the system's architecture. Sensory processing in olfactory systems is organized across olfactory bulb (OB) glomeruli, wherein axons of peripheral sensory neurons expressing the same olfactory receptor (OR) co-terminate to transmit receptor-specific activity to mitral/tufted cells, projection neurons in the olfactory bulb. Understanding how receptors map to glomeruli is therefore critical to understanding olfaction.Here we describe a method to enrich low-abundant OR mRNAs from the mouse OB for spatial transcriptomics to generate high-throughput mapping of receptors to glomeruli [2]. Our method combines sequential sectioning along the anteroposterior, dorsoventral, and mediolateral axes with target capture enrichment sequencing to overcome low-abundance target expression. This strategy spatially mapped 86% of olfactory receptors across the olfactory bulb and uncovered a relationship between olfactory receptor sequence and glomerular position.

Decoding the olfactory map through targeted transcriptomics links murine olfactory receptors to glomeruli.

Sensory processing in olfactory systems is organized across olfactory bulb glomeruli, wherein axons of peripheral sensory neurons expressing the same olfactory receptor co-terminate to transmit receptor-specific activity to central neurons. Understanding how receptors map to glomeruli is therefore critical to understanding olfaction. High-throughput spatial transcriptomics is a rapidly advancing field, but low-abundance olfactory receptor expression within glomeruli has previously precluded high-throughput mapping of receptors to glomeruli in the mouse. Here we combined sequential sectioning along the anteroposterior, dorsoventral, and mediolateral axes with target capture enrichment sequencing to overcome low-abundance target expression. This strategy allowed us to spatially map 86% of olfactory receptors across the olfactory bulb and uncover a relationship between OR sequence and glomerular position.

Estrogen and sex-dependent loss of the vocal learning system in female zebra finches.

Sex hormones alter the organization of the brain during early development and coordinate various behaviors throughout life. In zebra finches, song learning is limited to males, with the associated song learning brain pathways only maturing in males and atrophying in females. While this atrophy can be prevented by treating females with exogenous estrogen during early post-hatch development, the requirement of estrogen during normal male song system development is uncertain. For the first time in songbirds, we administered exemestane, a potent third generation estrogen synthesis inhibitor, from the day of hatching until adulthood in order to reassess the role of estrogen in song circuit development. We examined the behavior, brain anatomy, and transcriptomes of individual song nuclei in these pharmacologically manipulated animals. We found that males with long-term exemestane treatment had diminished male-specific plumage and impaired song learning, but minimal effect on song nuclei sizes and their specialized transcriptome. Consistent with prior findings, females with long-term estrogen treatment retained a functional song system with song nuclei that had specialized gene expression similar, but not identical to males. We also observed that different song nuclei responded to estrogen manipulation differently, with Area X in the striatum being the most altered by estrogen modulation. These findings support the hypothesis that song learning is an ancestral trait in both sexes that was subsequently suppressed in females of some species and that estrogen has come to play a critical role in modulating this suppression as well as refinement of song learning.

Concentration-Dependent Recruitment of Mammalian Odorant Receptors.

A fundamental challenge in studying principles of organization used by the olfactory system to encode odor concentration information has been identifying comprehensive sets of activated odorant receptors (ORs) across a broad concentration range inside freely behaving animals. In mammals, this has recently become feasible with high-throughput sequencing-based methods that identify populations of activated ORs in vivo In this study, we characterized the mouse OR repertoires activated by the two odorants, acetophenone (ACT) and 2,5-dihydro-2,4,5-trimethylthiazoline (TMT), from 0.01% to 100% (v/v) as starting concentrations using phosphorylated ribosomal protein S6 capture followed by RNA-Seq. We found Olfr923 to be one of the most sensitive ORs that is enriched by ACT. Using a mouse line that genetically labels Olfr923-positive axons, we provided evidence that ACT activates the Olfr923 glomeruli in the olfactory bulb. Through molecular dynamics stimulations, we identified amino acid residues in the Olfr923 binding cavity that facilitate ACT binding. This study sheds light on the active process by which unique OR repertoires may collectively facilitate the discrimination of odorant concentrations.

Cross-platform prediction of gene expression signatures.

Gene expression signatures can predict the activation of oncogenic pathways and other phenotypes of interest via quantitative models that combine the expression levels of multiple genes. However, as the number of platforms to measure genome-wide gene expression proliferates, there is an increasing need to develop models that can be ported across diverse platforms. Because of the range of technologies that measure gene expression, the resulting signal values can vary greatly. To understand how this variation can affect the prediction of gene expression signatures, we have investigated the ability of gene expression signatures to predict pathway activation across Affymetrix and Illumina microarrays. We hybridized the same RNA samples to both platforms and compared the resultant gene expression readings, as well as the signature predictions. Using a new approach to map probes across platforms, we found that the genes in the signatures from the two platforms were highly similar, and that the predictions they generated were also strongly correlated. This demonstrates that our method can map probes from Affymetrix and Illumina microarrays, and that this mapping can be used to predict gene expression signatures across platforms.