Bluebrain/Neurom: V1.2.0

Palacios, J.; lidakanari,; Zisis, Eleftherios; MikeG,; Vanherpe, Liesbeth; Courcol, Jean-Denis; Amsalem, Oren

doi:10.5281/zenodo.209498

Cited by 2 publications

(1 citation statement)

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“…To construct the different combinations of feature vectors we employed the ephys features used in training for model generation, morphological features, and, finally, the model parameter set with least training error. For morphological feature extraction and analysis we used the python package NeuroM ( Palacios et al, 2016 ) to extract the following features from the input .swc reconstructions: soma_surface, soma_radius, and length, area, volume, taper_rate for all the neurites (tree of sections), i.e., apical, basal dendrite, axon. For model-based feature construction we only consider parameters shared by all cells to prevent trivial differences between excitatory and inhibitory cells from dominating the outcome (e.g., no apical dendrites for aspiny/sparsely spiny cell reconstructions or differences in conductance recipe, Table S1 , built into the model generation pipeline).…”

Section: Methodsmentioning

confidence: 99%

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

Nandi

Chartrand²,

Geit³

et al. 2022

Cell Reports

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Section: Methodsmentioning

confidence: 99%

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

Nandi

Chartrand²,

Geit³

et al. 2022

Cell Reports

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

Nandi

Chartrand

Geit

et al. 2020

Preprint

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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...

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Bluebrain/Neurom: V1.2.0

Cited by 2 publications

References 0 publications

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

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

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

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