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.

Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture

“…While in vitro and in vivo studies in model organisms ( van Vugt et al, 2020 ; Wang et al, 2013 ) can test these hypotheses at the single-neuron level, causal manipulations and large-scale recordings of neuronal networks embedded in the human brain are severely limited. Here, we apply an approach analogous to multimodal single-cell profiling ( Bomkamp et al, 2019 ) and examine the transcriptomic basis of neuronal dynamics at the macroscale.…”

“…( B ) Timescale gradient is significantly correlated with expression of genes known to alter synaptic and neuronal membrane time constants, as well as inhibitory cell-type markers, but ( C ) members within a gene family (e.g., NMDA receptor subunits) can be both positively and negatively associated with timescales, consistent with predictions from in vitro studies. ( D ) Macroscale timescale-transcriptomic correlation captures association between RNA-sequenced expression of the same genes and single-cell timescale properties fit to patch clamp data from two studies, and the correspondence improves for genes (separated by quintiles) that are more strongly correlated with timescale (solid: N = 170 [ Tripathy et al, 2017 ], dashed: N = 4168 genes [ Bomkamp et al, 2019 ]; horizontal lines: correlation across all genes from the two studies, ρ = 0.36 and 0.25, p < 0.001 for both). ( E ) T1w/T2w gradient is regressed out from timescale and gene expression gradients, and a partial least squares (PLS) model is fit to the residual maps.…”

Sex Differences in Biophysical Signatures across Molecularly Defined Medial Amygdala Neuronal Subpopulations

“…The temporal sequence of APs or spiking pattern, is one defining feature of neuronal subclass identity (Petilla Interneuron Nomenclature Group, 2008;Aljadeff et al, 2016;Ha and Cheong, 2017). Spiking patterns are influenced by a number of intrinsic electrophysiological parameters such as resting membrane potential, membrane resistance and cell capacitance (Petilla Interneuron Nomenclature Group, 2008;Boada, 2013;Li and Tsien, 2017;Tapia et al, 2018;Bomkamp et al, 2019). To characterize the spiking patterns of MeA Dbx1-lineage and Foxp2-lineage neurons in females and males, we performed whole-cell patch clamp on YFP-expressing neurons in Dbx1 cre ;RYFP and Foxp2 cre ;RYFP mice.…”

Postnatal Arx transcriptional activity regulates functional properties of PV interneurons

“…Although our RNAseq data support an ECM-dependent mechanism in the alterations of PVI membrane properties, we acknowledge that the excitable properties of neurons, which can be influenced by localization or phosphorylation, may not always align to the transcriptomic level of a particular ion channel. Although RNA-Seq transcriptomic profiling provides a comprehensive analysis of actively regulated genes, many studies have found no correlation or seemingly inverse relationships between the expression of various membrane ( Adelman et al., 2019 ; Bomkamp et al., 2019 ; Földy et al., 2016 ; Larson et al., 2016 ; Tripathy et al., 2017 ) and synaptic ( Fazel Darbandi et al., 2018 ; Harrington et al., 2016 ; Yook et al., 2019 ) genes with their electrophysiological properties. Thus, the relationship between gene expression and cellular/behavioral phenotypes is complex, and this complexity must be taken into account in future studies into the ion channel mechanisms mediating hypoexcitability of PVIs in Arx CKO mice.…”