Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell types
Werner Van Geit,
Soo Yeun Lee,
Posted 10 Apr 2020
bioRxiv DOI: 10.1101/2020.04.09.030239
Posted 10 Apr 2020
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.
- Downloaded 1,926 times
- Download rankings, all-time:
- Site-wide: 4,179 out of 100,263
- In neuroscience: 596 out of 17,843
- Year to date:
- Site-wide: 765 out of 100,263
- Since beginning of last month:
- Site-wide: None out of 100,263
Downloads over time
Distribution of downloads per paper, site-wide
- 20 Oct 2020: Support for sorting preprints using Twitter activity has been removed, at least temporarily, until a new source of social media activity data becomes available.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!