Keller, DanielMeystre, JulieVeettil, Rahul V.Burri, OlivierGuiet, RomainSchuermann, FelixMarkram, Henry2019-08-142019-08-142019-08-142019-08-0610.3389/fnana.2019.00078https://infoscience.epfl.ch/handle/20.500.14299/159758WOS:000478907500001Obtaining 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.Anatomy & MorphologyNeurosciencesNeurosciences & Neurologycell densityrat braininhibitory interneuronssomatosensory cortexcompositioncell typescell countingneuronal distributiongabaergic interneuronscell-typescalbindinratcalretininneocortexneuronsparvalbumindiversitytext::journal::journal article::research article