CellNeurosciencePeer-reviewed journal article
Toward an integrated classification of neuronal cell types: morphoelectric and transcriptomic characterization of individual GABAergic cortical neurons
Nathan W. GouwensiD, Staci A. SorenseniD, Fahimeh BaftizadehiD, Agata BudzilloiD, Brian LeeiD, Tim JarskyiD, Lauren AlfileriD, Katherine S. BakeriD, Eliza R. BarkaniD, Kyla BerryiD, Darren BertagnolliiD, Kris BickleyiD, Jasmine BombeniD, Thomas BrauniD, Krissy BrouneriD, Tamara CasperiD, Kirsten CrichtoniD, Tanya L. DaigleiD, Rachel A. DalleyiD, Rebecca A. de FratesiD, Nick DeeiD, Tsega DestaiD, Samuel Dingman LeeiD, Nadezhda I. DotsoniD, Tom EgdorfiD, , Hongkui ZengiD
Abstract
Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. We found that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features. We observed that these properties could vary continuously between t-types, which limited the ability to predict specific t-types from other data modalities. Despite that, the data support the presence of at least 20 interneuron met-types that have congruent morphological, electrophysiological, and transcriptomic properties. HighlightsO_LIPatch-seq data obtained from >3,700 GABAergic cortical interneurons C_LIO_LIComprehensive characterization of morpho-electric features of transcriptomic types C_LIO_LI20 interneuron met-types that have congruent properties across data modalities C_LIO_LIDifferent Sst met-types preferentially innervate different cortical layers C_LI
- organism: mouse
- assay: scRNA-seq
- assay: electronic health records
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- approach: experimental assay
- Citations
- 538
- FWCI
- 31.49
- Field percentile
- top 1%
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