Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell types

Abstract

Highlights1. Generation and evaluation of more than 130 million single-cell models with active conductances along the reconstructed morphology faithfully recapitulate the electrophysiology of 230 in vitro experiments. 2. Optimized ion channel conductances along the cellular morphology ('all-active') are characteristic of model complexity and offer enhanced biophysical realism. 3. Ion channel conductance vectors of all-active models classify transcriptomically defined cell-types. 4. Cell type differences in ion channel conductances predicted by the models correlate with experimentally measured single-cell gene expression differences in inhibitory (Pvalb, Sst, Htr3a) and excitatory (Nr5a1, Rbp4) classes. 5. A set of ion channel conductances identified by comparing between cell type model populations explain electrophysiology differences between these types in simulations and brain slice experiments. 6. All-active models recapitulate multimodal properties of excitatory and inhibitory cell types offering a systematic and causal way of linking differences between them. AbstractIdentifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly important for classification. Next, using models of genetically defined classes, we show that differences in specific conductances predicted from the models reflect differences in gene expression in excitatory and inhibitory cell types as experimentally validated by single-cell RNA-sequencing. The differences in these conductances, in turn, explain many of the electrophysiological differences observed between cell types. Finally, we show the robustness of the herein generated single-cell models as representations and realizations of specific cell types in face of biological variability and optimization complexity. Our computational effort generated models that reconcile major single-cell data modalities that define cell types allowing for causal relationships to be examined. Results Large-scale generation of single-cell modelsThe excitatory and inhibitory neurons assayed in the current work originate from mouse primary visual cortex (V1) as well as neighboring cortical areas...

Modeling Reveals Human–Rodent Differences in H-Current Kinetics Influencing Resonance in Cortical Layer 5 Neurons

“…However, less explored are the active, voltage-gated ion channels of human cortical neurons. The hyperpolarization-activated cation (h-) channel is a voltage-gated channel of increasing interest, considering recent findings showing that its expression may serve to differentiate cell types ( Nandi et al 2020 ). Additionally, it has long been attributed with a pacemaking role and hence of potential importance in functionally relevant brain rhythms ( Fries 2005 ; Buzsaki 2006 ; Anastassiou et al 2011 ; Akam and Kullmann 2014 ; Womelsdorf et al 2014 ; Hanslmayr et al 2019 ; Vaz et al 2019 ).…”

“…The decision to more precisely model the activity of the h-current is motivated by the clear preponderance of h-channels in human L5 neurons ( Moradi Chameh et al 2020 ), their complex role in regulating neuronal excitability ( Biel et al 2009 ; Dyhrfjeld-Johnsen et al 2009 ), their role in dictating cell types ( Nandi et al 2020 ), and their hypothesized role in driving subthreshold resonance ( Hu et al 2002 , 2009 ; Ulrich 2002 ; Zemankovics et al 2010 ; Kalmbach et al 2018 ). Our neuron model contains a new articulation of the activity of the h-current in the human setting, derived entirely by “fitting” the model to current clamp data.…”

Modeling reveals human-rodent differences in h-current kinetics influencing resonance in cortical layer 5 neurons

“…However, less explored are the active, voltage-gated ion channels of human cortical neurons. The hyperpolarization-activated cation (h-) channel is a voltage-gated channel of increasing interest, considering recent findings showing that its expression may serve to differentiate cell types (Nandi et al, 2020). Additionally, it has long been attributed with a pacemaking role and hence of potential importance in functionally relevant brain rhythms (Akam and Kullmann, 2014; Buzsaki, 2006; Womelsdorf et al, 2014; Fries, 2005; Anastassiou et al, 2011; Hanslmayr et al, 2019; Vaz et al, 2019).…”

“…The decision to more precisely model the activity of the h-current is motivated by the clear preponderance of h-channels in human L5 neurons (Chameh et al, 2019), their complex role in regulating neuronal excitability (Dyhrfjeld-Johnsen et al, 2009; Biel et al, 2009), their role in dictating cell types (Nandi et al, 2020), and their hypothesized role in driving subthreshold resonance (Kalmbach et al, 2018; Hu et al, 2002, 2009; Zemankovics et al, 2010; Ulrich, 2002). Our neuron model contains a new articulation of the activity of the h-current in the human setting, derived entirely by “fitting” the model to current clamp data.…”

Multi-modal characterization and simulation of human epileptic circuitry

“…To generate human GC single-neuron models we used a 3-stage approach leveraging a genetic optimization framework which relies on comparisons between experimental and model electrophysiology features for a particular morphology reconstruction ( Fig. S4, (25)(26)(27)). Using this optimization framework, we generated GC models accounting for active ionic conductances along their entire soma, axonal and dendritic arbor.…”