Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes.

Claire Bomkamp,Shreejoy J Tripathy,Paul Pavlidis,Jens Hjerling-Leffler,Ann Marie Craig,Carolina Bengtsson Gonzales,Lyle J Graham

doi:10.1371/journal.pcbi.1007113

Abstract

In order to further our understanding of how gene expression contributes to key functional properties of neurons, we combined publicly accessible gene expression, electrophysiology, and morphology measurements to identify cross-cell type correlations between these data modalities. Building on our previous work using a similar approach, we distinguished between correlations which were “class-driven,” meaning those that could be explained by differences between excitatory and inhibitory cell classes, and those that reflected graded phenotypic differences within classes. Taking cell class identity into account increased the degree to which our results replicated in an independent dataset as well as their correspondence with known modes of ion channel function based on the literature. We also found a smaller set of genes whose relationships to electrophysiological or morphological properties appear to be specific to either excitatory or inhibitory cell types. Next, using data from PatchSeq experiments, allowing simultaneous single-cell characterization of gene expression and electrophysiology, we found that some of the gene-property correlations observed across cell types were further predictive of within-cell type heterogeneity. In summary, we have identified a number of relationships between gene expression, electrophysiology, and morphology that provide testable hypotheses for future studies.

Highlights

Two prominent features that distinguish neurons from other cells are their electrical activity and their characteristic morphology
Transcriptomic correlates of neuronal diversity the cell type classification used here is somewhat crude and that there is overlap between cell types as we have defined them, as well as variability within types, but chose to define cell types in this way because it allows us to combine data from the RNA-seq and electrophysiology/ morphology datasets
We introduced a third model, the interaction model (P~G+C+G C), which tested whether the relationship between gene expression and the property in question was significantly different between excitatory and inhibitory cell types

Summary

Introduction

Two prominent features that distinguish neurons from other cells are their electrical activity and their characteristic morphology. The specific pattern of electrophysiological activity displayed by a given neuron is a core property of its identity as one type of neuron or another. Neuronal cell types defined according to their electrophysiological or morphological characteristics show substantial correspondence with one another as well as with those defined using classification schemes based on transcriptomic criteria [1]. Electrophysiological characteristics of neurons, as well as their connectivity patterns, give rise to the computational properties of a given circuit [2,3]. Understanding the origins of neuronal electrophysiology and morphology is an important step in understanding the mechanisms of brain function, both in the context of basic research and in the search for treatments for neuropsychiatric disorders

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