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Lookup NU author(s): Dr Srikanth RamaswamyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 Roussel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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 characterized 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 metypes 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 19 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(s): Roussel Y, Veraszto C, Rodarie D, Damart T, Reimann M, Ramaswamy S, Markram H, Keller D
Publication type: Article
Publication status: Published
Journal: PLoS Computational Biology
Year: 2023
Volume: 19
Issue: 1
Online publication date: 05/01/2023
Acceptance date: 26/03/2022
Date deposited: 05/03/2025
ISSN (print): 1553-734X
ISSN (electronic): 1553-7358
Publisher: Public Library of Science
URL: https://doi.org/10.1371/journal.pcbi.1010058
DOI: 10.1371/journal.pcbi.1010058
Data Access Statement: Code is under open sourcing process and is publicly available at https://github.com/BlueBrain/me-features-to-mo-ID-mapping. Downloading of Allen Institute for Brain Science data was not incorporated in the code and is left at users discretion due to license issues. We recommend to use allensdk package (https://github.com/AllenInstitute/AllenSDK) to obtain the Allen Institute for Brain Science data. More details are provided in the README.md file of our public repository.
PubMed id: 36602951
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