Mancarci, B. O. | Search bioRxiv

bioRxiv2017

Transcriptomic Correlates Of Neuron Electrophysiological Diversity

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating transcriptomics with intracellular electrophysiology. Using a brain-wide dataset of 34 neuron types, we identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 electrophysiological parameters. The majority of these correlations were consistent in an independent sample of 12 visual cortex cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. These results suggest that despite the complexity linking gene expression to electrophysiology, there are likely some general principles that govern how individual genes establish phenotypic diversity across very different cell types.

neuroscience↗

bioRxiv2016

NeuroExpresso: A cross-laboratory database of brain cell-type expression profiles with applications to marker gene identification and bulk brain tissue transcriptome interpretation

The identification of cell type marker genes, genes highly enriched in specific cell types, plays an important role in the study of the nervous system. In particular, marker genes can be used to identify cell types to enable studies of their properties. Marker genes can also aid the interpretation of bulk tissue expression profiles by revealing cell type specific changes.\n\nWe assembled a database, NeuroExpresso, of publicly available mouse brain cell type-specific gene expression datasets. We then used stringent criteria to select marker genes highly expressed in individual cell types. We found a substantial number of novel markers previously unknown in the literature and validated a subset of them using in silico analyses and in situ hybridization. We next demonstrate the use of marker genes in analysis of whole tissue data by summarizing their expression into \"cell type profiles\" that can be thought of as surrogates for the relative abundance of the cell types across the samples studied.\n\nFurther analysis of our cell type-specific expression database confirms some recent findings about brain cell types along with revealing novel properties, such as Ddc expression in oligodendrocytes. To facilitate further use of this expanding database, we provide a user-friendly web interface for the visualization of expression data.\n\nSignificance StatementCell type markers are powerful tools in the study of the nervous system that help reveal properties of cell types and acquire additional information from large scale expression experiments. Despite their usefulness in the field, known marker genes for brain cell types are few in number. We present NeuroExpresso, a database of brain cell type specific gene expression profiles, and demonstrate the use of marker genes for acquiring cell type specific information from whole tissue expression. The database will prove itself as a useful resource for researchers aiming to reveal novel properties of the cell types and aid both laboratory and computational scientists to unravel the cell type specific components of brain disorders.

neuroscience↗