Transcriptomic evidence that von Economo neurons are regionally specialized extratelencephalic-projecting excitatory neurons

Abstract

von Economo neurons (VENs) are bipolar, spindle-shaped neurons restricted to layer 5 of human frontoinsula and anterior cingulate cortex that appear to be selectively vulnerable to neuropsychiatric and neurodegenerative diseases, although little is known about other VEN cellular phenotypes. Single nucleus RNA-sequencing of frontoinsula layer 5 identifies a transcriptomically-defined cell cluster that contained VENs, but also fork cells and a subset of pyramidal neurons. Cross-species alignment of this cell cluster with a well-annotated mouse classification shows strong homology to extratelencephalic (ET) excitatory neurons that project to subcerebral targets. This cluster also shows strong homology to a putative ET cluster in human temporal cortex, but with a strikingly specific regional signature. Together these results suggest that VENs are a regionally distinctive type of ET neuron. Additionally, we describe the first patch clamp recordings of VENs from neurosurgically-resected tissue that show distinctive intrinsic membrane properties relative to neighboring pyramidal neurons.

Human cortical expansion involves diversification and specialization of supragranular intratelencephalic-projecting neurons

“…Data alignment methods have been developed to match t-types across conditions where variability across data sets is dramatically higher than variability between t-types 34,35,36 . These strategies have been successful for comparisons of cells in different cortical regions or even in different species using the t-type classifications used in the current study 11,37,22 . Here, human Patch-seq cells were mapped using the cell type classification workflow in Seurat (V3) 34,35 , after first filtering out genes potentially associated with the undesirable sources of variation described above (also see Methods).…”

Cerebellar nuclei evolved by repeatedly duplicating a conserved cell type set

“…To our knowledge, however, none of these processes have been demonstrated in vertebrate brain evolution at cell type resolution. Doing so requires a comprehensive comparison of cell types across regions (Yao et al 2020) and species (Hodge et al 2019;Bakken et al 2020;Boldog et al 2018;Hodge et al 2020;Tosches et al 2018;Krienen et al 2019;Peng et al 2019;Hoang et al 2019;Norimoto et al 2020;Khrameeva et al 2020) in a system that contains different numbers of homologous regions in different species.…”

Common cell type nomenclature for the mammalian brain

“…By aligning cell types across species, the projection targets in mice can be hypothetically extrapolated to putative projection targets in human or other mammalian species. For example, von Economo neurons are likely to project subcortically ( Hodge et al, 2020 ). For GABAergic interneurons, developmental origin may define cell types by their canonical marker gene profile established early in development, with Pvalb and Sst labeling cell types derived from the medial ganglionic eminence and Vip , Sncg , and Lamp5 labeling cell types derived from the caudal ganglionic eminence ( DeFelipe et al, 2013 ).…”

“…In the retina, neurons with shared morphology also have consistentconnectivity ( Jonas and Kording, 2015 ), spacing, arbor density, arbor stratification ( Seung and Sümbül, 2014 ), and gene expression signatures ( Macosko et al, 2015 ), often with one-to-one correspondences between phenotype and function ( Zeng and Sanes, 2017 ). However, studies combining scRNA-seq with traditional morphological and electrophysiological characterizations in the brain have found a more complicated relationship in the brain than in retina, with cell types defined by morphology and electrophysiology sometimes containing cells from several cell types defined using gene expression ( Gouwens et al, 2020 ; Kozareva et al, 2020 ), and some transcriptomically defined types containing cells with multiple morphologies ( Hodge et al, 2020 ; Hodge et al, 2019 ). Further complicating classification is the overlay of discrete cell type distinctions with graded/continuous properties such as cortical depth ( Berg et al, 2020 ), anterior/posterior and other trajectories across neocortex ( Hawrylycz et al, 2012 ), activity-dependent cell state ( Wu et al, 2017 ), or all simultaneously ( Yao et al, 2020b ).…”

“…Substantial progress has been made toward solving this challenge of ‘alignment’, whereby datasets collected with genomics assays such as scRNA-seq or snATAC-seq can be used to anchor diverse cell types in a common analysis space ( Barkas et al, 2018 ; Butler et al, 2018 ; Johansen and Quon, 2019 ). Alignment has proven effective for matching cell type sequence data collected on different platforms, across multiple data modalities, and even between species where few homologous marker genes show conserved patterns ( Bakken et al, 2020a ; Bakken et al, 2020b ; Hodge et al, 2020 ; Hodge et al, 2019 ; Yao et al, 2020a ). When combined with experimental methods such as Patch-seq ( Cadwell et al, 2016 ; Fuzik et al, 2016 ; Gouwens et al, 2020 ; Scala et al, 2020 ), which involves application of electrophysiological recording and morphological analysis of single patch-clamped neurons followed by scRNA-seq of cell contents, autoencoder-based dimensionality reduction ( Gala et al, 2019 ) can extend these alignments to bridge distinct modalities.…”