A community-based transcriptomics classification and nomenclature of neocortical cell types

TL;DR

This paper proposes a standardized, hierarchical, and community-adopted transcriptome-based taxonomy for classifying neocortical cell types, leveraging single-cell transcriptomics data to unify cellular classification across species and developmental stages.

Contribution

It introduces a community-driven framework for a transcriptome-based taxonomy of neocortical cells, emphasizing standardization, hierarchy, and adaptability to new data.

Findings

Identification of clear cell type clusters conserved across species

Proposal of a standardized nomenclature for neocortical cell types

Framework for integrating diverse data sources into a unified taxonomy

Abstract

To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological features of neurons and glia, while productive, have not resulted in a unified taxonomy of neural cell types. The recent development of single-cell transcriptomics has enabled, for the first time, systematic high-throughput profiling of large numbers of cortical cells and the generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data have revealed the existence of clear clusters, many of which correspond to cell types defined by traditional criteria, and which are conserved across cortical areas and species. To capitalize on these innovations and advance the field, we, the Copenhagen Convention Group, propose the community adopts a transcriptome-based taxonomy of the cell types in the adult mammalian neocortex. This core classification should be ontological, hierarchical and use a standardized nomenclature. It should be configured to flexibly incorporate new data from multiple approaches, developmental stages and a growing number of species, enabling improvement and revision of the classification. This community-based strategy could serve as a common foundation for future detailed analysis and reverse engineering of cortical circuits and serve as an example for cell type classification in other parts of the nervous system and other organs.