DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible

Systematic analysis of expression signatures of neuronal subpopulations in the VTA

Gene expression profiling across various brain areas at the single-cell resolution enables the identification of molecular markers of neuronal subpopulations and comprehensive characterization of their functional roles. Despite the scientific importance and experimental versatility, systematic methods to analyze such data have not been established yet. To this end, we developed a statistical approach based on in situ hybridization data in the Allen Brain Atlas and thereby identified specific genes for each type of neuron in the ventral tegmental area (VTA). This approach also allowed us to demarcate subregions within the VTA comprising specific neuronal subpopulations. We further identified WW domain-containing oxidoreductase as a molecular marker of a population of VTA neurons that co-express tyrosine hydroxylase and vesicular glutamate transporter 2, and confirmed their region-specific distribution by immunohistochemistry. The results demonstrate the utility of our analytical approach for uncovering expression signatures representing specific cell types and neuronal subpopulations enriched in a given brain area.This work was supported by the grants from National Research Foundations of Korean Ministry of Science and ICT (2018M3C7A1024152, 2018R1A3B1052079, 2019M3A9B6066967, and 2019R1A6A1A10073437) and the Institute for Basic Science (IBS-R013-A1)

A tool for identification of genes expressed in patterns of interest using the Allen Brain Atlas

Motivation: Gene expression patterns can be useful in understanding the structural organization of the brain and the regulatory logic that governs its myriad cell types. A particularly rich source of spatial expression data is the Allen Brain Atlas (ABA), a comprehensive genome-wide in situ hybridization study of the adult mouse brain. Here, we present an open-source program, ALLENMINER, that searches the ABA for genes that are expressed, enriched, patterned or graded in a user-specified region of interest