Phenotypic variation of transcriptomic cell types in mouse motor cortex

Abstract

Cortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties1,2. Most existing neural taxonomies are based on either transcriptomic3,4 or morpho-electric5,6 criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells7. Here we used Patch-seq8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (those expressing Vip, Pvalb, Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neuronal types in the neocortex do not always form discrete entities. Instead, neurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.

Layer 6A pyramidal cells subtypes form synaptic microcircuits with distinct functional and structural properties

“…The remaining interneurons are the so-called non fast-spiking (nFS) neurons which comprise a large group of irregular-spiking, late spiking, and burst spiking interneurons (Kawaguchi and Kubota 1996;Defelipe et al 2013;Emmenegger et al 2018). Both FS and nFS interneurons are broad families with different transcriptomic, electrophysiological and morphological phenotypes (Gouwens et al 2020;Scala et al 2020;Yuste et al 2020). Excitatory synapses onto FS interneurons are initially strong (i.e.…”

Consistent cross-modal identification of cortical neurons with coupled autoencoders

“…We used only electrophysiology features to infer the expression patterns for all genes in the cross-modal setting, and show results for the same subset of genes as before. The striking similarity of these expression patterns ( We directly tested the idea that pre-trained coupled autoencoders can be used to predict unobserved cross-modal features in independent experiments by using two recent Patch-seq datasets, 22,23 which include 107 and 524 inhibitory neurons from mouse motor cortex respectively.…”

“…The Patch-seq data used in the main text is used as the reference dataset, and is referred to as the Gouwens et Previous studies have shown that the cell type diversity of inhibitory neurons is essentially conserved across brain areas. 8 Therefore, even though both, the Scala et al 2019 andthe Scala et al 2020 datasets profile neurons from mouse motor cortex, we can hope to use the mouse visual cortex Patch-seq dataset used in this study to serve as a meaningful reference. Nearly 5% of the genes that were used as input for the coupled autoencoders were either not measured, or were missing from the transcriptomic profiles of neurons in both these datasets.…”

Manifold learning analysis suggests novel strategies for aligning single-cell multi-modalities and revealing functional genomics for neuronal electrophysiology

“…1A). In particular, we focused on two major brain regions, mouse visual cortex and motor cortex, and used the latest Patch-seq data from Allen Brain Atlas in the BRAIN Initiative 5,12,13 (Methods). The machine learning methods for alignment include linear manifold alignment (LM), nonlinear manifold alignment (NMA), manifold warping (MW), Canonical Correlation Analysis (CCA), Principal Component Analysis (PCA, no alignment) and t-Distributed Stochastic Neighbor Embedding (t-SNE, no alignment).…”

“…For instance, previous correlation-based analyses found individual genes whose expression levels linearly correlate with electrophysiological features in excitatory and inhibitory neurons 3,4 . Besides, recent studies have also identified several cell types from different modalities that share many cells (e.g., me-type), suggesting the linkages across modalities in these cells 2,5 . However, understanding the molecular mechanisms underlying multi-modalities that typically involve multiple genes is still challenging.…”