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

Abstract

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

Fig. 1. Non-transcriptomics cortical cell-type classifications.

a,b, Morphological characterization and classification of neurons (a) and glial cells (b) by Ramón y Cajal (1904). c, Diagram showing the connections of different types of interneurons with pyramidal cells. Adapted from Szentágothai (1975). d, Definition of GABAergic interneuron classes based on non-overlapping and combinatorial marker gene expression. e, Correlation of firing properties with class markers. f, Cortical cell type classification based on intrinsic firing properties (Petilla convention). g, Complex relationships between cellular morphology, marker-gene expression and intrinsic firing properties based on multimodal analysis. h, Comprehensive morphological and physiological classifications of cortical cell types. Images in a,b reprinted with permission from ref. , Cajal Institute; in c, adapted with permission from ref. , Elsevier; in d, adapted with permission from ref. , Oxford Univ. Press; in e, adapted with permission from ref. , Society for Neuroscience; in f and g, adapted with permission from refs. ^,, respectively, Springer Nature; in h, adapted with permission from ref. , Cell Press.

Fig. 2. Transcriptomics classifications of cortical cell types.

a, Single-cell transcriptome analysis reveals a molecular diversity of mouse cell types, with relatively invariant interneuron and non-neuronal types across cortical areas but significant variation in excitatory neurons. b, Major interneuron classes are specified by distinct transcription factor codes. c, Single-cell transcriptomics of mouse GABAergic interneuron development demonstrates gradual changes in gene expression underlying developmental maturation and fate bifurcations as cells become postmitotic. d, Gene families shaping cardinal GABAergic neuron type include neuronal connectivity, ligand receptors, electrical signaling, intracellular signal transduction, synaptic transmission and gene transcription. These gene families assemble membrane-proximal molecular machines that customize input–output connectivity and properties in different GABAergic types. e, Single-cell transcriptomics allows cross-species comparisons and shows conservation of major cell classes from reptiles to mammals, with conserved transcription factors but some species-specific effectors (turtle data). TF, transcription factor. Images in a and c adapted with permission from refs. ^,, respectively, Springer Nature; in b, adapted with permission from ref. , Elsevier; in d, adapted with permission from ref. , Cell Press; in e, adapted with permission from refs. ^,, Elsevier and AAAS, respectively.

Fig. 3. Correspondence across phenotypes of cortical neuron types.

a, Quantitative morphological clustering and electrophysiological feature variation between major inhibitory neuron classes using transgenic mouse lines (modified from Figs. 1 and 2 from ref. ). b, Convergent physiological, anatomical and transcriptomic evidence for a distinctive rosehip layer 1 inhibitory neuron type in human cortex that differs from neighboring neurogliaform cells. c, Morphological and physiological differences between layer 1 neurogliaform and single bouquet neurons shown by patch-seq analysis. Scale bars as in b. d, RNA-seq analysis of retrogradely labeled neurons in mouse primary visual cortex show distinctive projections of excitatory subclasses, but overlapping projections for finer transcriptomic cell types. Images in a adapted with permission from ref. , Oxford Univ. Press; in b–d, adapted with permission from refs. ^, and , respectively, Springer Nature.

Fig. 4. Challenges for transcriptomic classification.

a, Gradients in morphological size and complexity across the rostrocaudal extent of the cortex. b, Graded transcriptomic variation across the human cortex encodes rostrocaudal position on the cortical sheet. c, Transcriptomic cell types can be aligned across species based on shared molecular specification, but often at a lower level of resolution than the finest types observed in a given species. Images in a adapted with permission from ref. , Oxford Univ. Press; in b and c, adapted with permission from refs. and , respectively, Springer Nature.

Fig. 5. Transcriptome based taxonomy, probabilistic cell types, and cell-type knowledge graphs.

a, A transcriptome-based cell-type taxonomy is constructed from scRNA-seq data, related epigenomic datasets and neuroanatomy, b, Cell types are initially defined based on transcriptomic signatures in a probabilistic manner with multiresolution clustering and statistical analysis to identify robustness and variability. c, Reproducible gene expression patterns identify hierarchies of putative cell types that are subject to further analyses and validation. d, Transcriptomic cell-type taxonomies form a basis for constructing cell-type knowledge graphs that summarize the present state of definable cell types. Multimodal assignment of data, such as morphology, electrophysiology and connectivity, is associated and reported with statistical variability over assigned types. A knowledge graph contains relevant and essential supporting information, such as supporting data for further analysis and mapping, descriptive annotation and ontology, and literature citations.