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A Derived Positional Mapping of Inhibitory Subtypes in the Somatosensory Cortex.

Authors
  • Keller, Daniel1
  • Meystre, Julie2
  • Veettil, Rahul V3
  • Burri, Olivier4
  • Guiet, Romain4
  • Schürmann, Felix1
  • Markram, Henry1, 2
  • 1 Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland. , (Switzerland)
  • 2 Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. , (Switzerland)
  • 3 Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel. , (Israel)
  • 4 Bioimaging and Optics Platform, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. , (Switzerland)
Type
Published Article
Journal
Frontiers in Neuroanatomy
Publisher
Frontiers Media SA
Publication Date
Jan 01, 2019
Volume
13
Pages
78–78
Identifiers
DOI: 10.3389/fnana.2019.00078
PMID: 31447655
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Obtaining a catalog of cell types is a fundamental building block for understanding the brain. The ideal classification of cell-types is based on the profile of molecules expressed by a cell, in particular, the profile of genes expressed. One strategy is, therefore, to obtain as many single-cell transcriptomes as possible and isolate clusters of neurons with similar gene expression profiles. In this study, we explored an alternative strategy. We explored whether cell-types can be algorithmically derived by combining protein tissue stains with transcript expression profiles. We developed an algorithm that aims to distribute cell-types in the different layers of somatosensory cortex of the developing rat constrained by the tissue- and cellular level data. We found that the spatial distribution of major inhibitory cell types can be approximated using the available data. The result is a depth-wise atlas of inhibitory cell-types of the rat somatosensory cortex. In principle, any data that constrains what can occur in a particular part of the brain can also strongly constrain the derivation of cell-types. This draft inhibitory cell-type mapping is therefore dynamic and can iteratively converge towards the ground truth as further data is integrated.

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