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

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.

Author summary

The behavior of neurons is governed by their electrical properties, for example how readily they respond to a stimulus or at what rate they are able to send signals. Additionally, neurons come in different shapes and sizes, and their shape defines how they can form connections with specific partners and thus function within the complete circuit. We know that these properties are governed by genes, acting acutely or during development, but we do not know which specific genes underlie many of these properties. Understanding how gene expression changes the properties of neurons will help in advancing our overall understanding of how neurons, and ultimately brains, function. This can in turn help to identify potential treatments for brain-related diseases. In this work, we aimed to identify genes whose expression showed a relationship with the electrical properties and shape measurements of different types of neurons. While our analysis does not identify causal relationships, our findings provide testable predictions for future research.