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Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons

View ORCID ProfileYann Roussel, View ORCID ProfileCsaba Verasztó, View ORCID ProfileDimitri Rodarie, View ORCID ProfileTanguy Damart, View ORCID ProfileMichael Reimann, View ORCID ProfileSrikanth Ramaswamy, View ORCID ProfileHenry Markram, Daniel Keller
doi: https://doi.org/10.1101/2021.11.24.469815
Yann Roussel
1Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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  • For correspondence: yann.roussel@epfl.ch
Csaba Verasztó
1Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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  • ORCID record for Csaba Verasztó
Dimitri Rodarie
1Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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Tanguy Damart
1Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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Michael Reimann
1Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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Srikanth Ramaswamy
1Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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Henry Markram
1Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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Daniel Keller
1Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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Abstract

Knowledge of the cell-type-specific composition of the brain is useful in order to understand the role of each cell type as part of the network. Here, we estimated the composition of the whole cortex in terms of well characterised morphological and electrophysiological inhibitory neuron types (me-types). We derived probabilistic me-type densities from an existing atlas of molecularly defined cell-type densities in the mouse cortex. We used a well-established me-type classification from rat somatosensory cortex to populate the cortex. These me-types were well characterized morphologically and electrophysiologically but they lacked molecular marker identity labels. To extrapolate this missing information, we employed an additional dataset from the Allen Institute for Brain Science containing molecular identity as well as morphological and electrophysiological data for mouse cortical neurons. We first built a latent space based on a number of comparable morphological and electrical features common to both data sources. We then identified 13 morpho-electrical clusters that merged neurons from both datasets while being molecularly homogeneous. The resulting clusters best mirror the molecular identity classification solely using available morpho-electrical features. Finally, we stochastically assigned a molecular identity to a me-type neuron based on the latent space cluster it was assigned to. The resulting mapping was used to derive inhibitory me-types densities in the cortex.

Author Summary The computational abilities of the brain arise from its organisation principles at the cellular level. One of these principles is the neuronal type composition over different regions. Since computational functions of neurons are best described by their morphological and electrophysiological properties, it is logical to use morpho-electrically defined cell types to describe brain composition. However, characterizing morpho-electrical properties of cells involve low-throughput techniques not very well suited to scan the whole brain. Thanks to recent progress on transcriptomic and immuno-staining techniques we are now able to get a more accurate snapshot of the mouse brain composition for molecularly defined cell types.How to link molecularly defined cell types with morpho-electrical cell types remains an open question. Several studies have explored this problem providing valuable three-modal datasets combining electrical, morphological and molecular properties of cortical neurons. The long-term goal of the Blue Brain Project (BBP) is to accurately model the mouse’s whole brain, which requires detailed biophysical models of neurons. Instead of going through the time-consuming process of producing detailed models from the three-modal datasets, we explored a time-saving method. We mapped the already available detailed morpho-electrical models from the BBP rat dataset to cells from a three-modal mouse dataset. We thus assigned a molecular identity to the neuron models allowing us to populate the whole mouse cortex with detailed neuron models.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • (CRediT abbreviations: C, DC, FoA, I, M, So, Va, Vi, WrD, WrR), (C, DC, I, M, Va, WrD, WrR), (C, DC, I, M, Va, WrD, WrR), (So, Va, WrD, WrR), (Va, WrD, WrR), (Va, WrD, WrR), (Su, Pa, Va, WrD, WrR), (Su, Pa, Va, WrD, WrR).

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 25, 2021.
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Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons
Yann Roussel, Csaba Verasztó, Dimitri Rodarie, Tanguy Damart, Michael Reimann, Srikanth Ramaswamy, Henry Markram, Daniel Keller
bioRxiv 2021.11.24.469815; doi: https://doi.org/10.1101/2021.11.24.469815
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Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons
Yann Roussel, Csaba Verasztó, Dimitri Rodarie, Tanguy Damart, Michael Reimann, Srikanth Ramaswamy, Henry Markram, Daniel Keller
bioRxiv 2021.11.24.469815; doi: https://doi.org/10.1101/2021.11.24.469815

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