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

By Anirban Nandi, Tom Chartrand, Werner Van Geit, Anatoly Buchin, Zizhen Yao, Soo Yeun Lee, Yina Wei, Brian Kalmbach, Brian Lee, Ed Lein, Jim Berg, Uygar Sümbül, Christof Koch, Bosiljka Tasic, Costas Anastassiou

Posted 10 Apr 2020
bioRxiv DOI: 10.1101/2020.04.09.030239

Identifying 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. ### Competing Interest Statement The authors have declared no competing interest.

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