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A community-based transcriptomics classification and nomenclature of neocortical cell types
Rafael Yuste, Michael Hawrylycz, Nadia Aalling, Detlev Arendt, Ruben Armananzas, Giorgio Ascoli, Concha Bielza, Vahid Bokharaie, Tobias Bergmann, Irina Bystron, Marco Capogna, Yoonjeung Chang, Ann Clemens, Christiaan de Kock, Javier DeFelipe, Sandra Dos Santos, Keagan Dunville, Dirk Feldmeyer, Richard Fiath, Gordon Fishell, Angelica Foggetti, Xuefan Gao, Parviz Ghaderi, Onur Gunturkun, Vanessa Jane Hall, Moritz Helmstaedter, Suzana Herculano-Houzel, Markus Hilscher, Hajime Hirase, Jens Hjerling-Leffler, Rebecca Hodge, Z. Josh Huang, Rafiq Huda, Yuan Juan, Konstantin Khodosevich, Ole Kiehn, Henner Koch, Eric Kuebler, Malte Kuhnemund, Pedro Larranaga, Boudewijn Lelieveldt, Emma Louise Louth, Jan Lui, Huibert Mansvelder, Oscar Marin, Julio Martínez-Trujillo, Homeira Moradi, Natalia Goriounova, Alok Mohapatra, Maiken Nedergaard, Pavel Němec, Netanel Ofer, Ulrich Pfisterer, Samuel Pontes, William Redmond, Jean Rossier, Joshua Sanes, Richard Scheuermann, Esther Serrano Saiz, Peter Somogyi, Gábor Tamás, Andreas Tolias, Maria Tosches, Miguel Turrero Garcia, Argel Aguilar-Valles, Hermany Munguba, Christian Wozny, Thomas Wuttke, Liu Yong, Hongkui Zeng, Ed S. Lein
TL;DR
Cortical cell diversity lacks a unified taxonomy, despite transcriptomics enabling comprehensive molecular profiling. The paper proposes a hierarchical, ontological, standardized transcriptome-based classification whose cell types can be compared across species and developmental stages.
Problem
Neocortical cell diversity lacks a unified taxonomy, while transcriptomics offers comprehensive quantitative profiling of cellular types and states.
Method
The authors propose a hierarchical, ontological taxonomy with standardized, portable nomenclature that can incorporate accumulating data across approaches, species, and developmental stages.
Results
Transcriptomic studies identify conserved major cortical GABAergic neuron classes across mammals and reptiles, alongside species-specific differences in gene expression, morphology, and distribution.
Takeaways & Limitations
A transcriptome-based classification provides a framework for quantitative cross-species comparison and future analysis of cortical circuits.
Takeaways & Limitations
Transcriptomic clusters can reflect dynamic cell states rather than discrete types, and transcriptomic data may not capture developmental history or phenotypes such as long-range connectivity.
Abstract
from arXiv · showhide
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.
Transcriptomics as the core framework for classifying cell types
Single-cell transcriptomics provides a quantitative, high-throughput molecular framework for systematically classifying neocortical cell diversity, while enabling cross-dataset and cross-species comparisons. Its probabilistic classifications offer predictive and experimental advantages but must accommodate cell-state variation, dynamic regulation, and mismatches with other phenotypes.
- Transcriptomics as the core framework for classifying cell types: Single-cell transcriptomics measures thousands of genes in many individual cells, enabling systematic and comprehensive analysis of cellular diversity at unprecedented scale.Datasets can range from tens of thousands to millions of cells.
- Transcriptomics as the core framework for classifying cell types: Transcriptomic clusters describe cell types probabilistically in a high-dimensional gene-expression landscape rather than through a small set of necessary and sufficient markers.This framework treats the transcriptome as an internal code describing each cell in spatiotemporal context.
- Transcriptomics as the core framework for classifying cell types: Transcriptomes can provide predictive links to cellular structure and function, support robust markers and genetic tools, connect disease-associated genes to their cellular loci, and align cell types across disparate datasets.Morphology and other phenotypes are partly encoded by genes, although a mature-cell transcriptome measured at one time point does not fully predict cellular properties.
- Transcriptomics as the core framework for classifying cell types: Transcriptomic classifications must account for gene-expression variation from cell state, differentiation, dynamic processes, and currently unmeasured aspects of genome regulation.These complexities may indicate that cell types are components of a landscape of possible states rather than wholly discrete entities.
- Transcriptomics as the core framework for classifying cell types: Potential mismatches between adult gene expression and phenotypes such as long-range connectivity do not negate transcriptomics as a core classification because genes link cell types to functions, disease, and manipulable genetic tools.Some connectivity patterns may be established early in development and may not correlate with adult gene expression.
- Transcriptomics as the core framework for classifying cell types: Comparative transcriptomics enables quantitative alignment across species, revealing conserved major GABAergic classes alongside species-specific differences in proportions, distributions, gene expression, morphology, and non-neuronal phenotypes.Mouse, turtle, and lizard share major somatostatin, parvalbuminlike, and HTR3A GABAergic classes, while human cortex and astrocytes show distinctive molecular features.
A probabilistic definition of cortical cell types
The paper proposes an operational, probabilistic definition of cortical cell types based on statistically defined clusters of measurable attributes. This framework should support hierarchical transcriptomic taxonomy while addressing variability, subdivision thresholds, and clusters representing cellular states rather than fixed types.
- Existing cell-type classifications emphasize different structural, functional, or identity-based criteria, making precise definition challenging.
- An operational definition can build on statistically defined clusters over measurable attributes, given cellular complexity and incomplete multimodal correspondence across species.
- A hierarchical transcriptomic taxonomy is a natural representation because cluster analysis can organize cell types into nested groups.
- Transcriptomic classifications require rigorous definitions of classes and their intra- and inter-class variability despite recurring cell-type patterns across competing approaches.
- Subdivision remains difficult because clusters may reflect pathological, developmental, or functional states rather than fixed cell types.
- A rigorous probabilistic framework should quantitatively define cell types independently of clustering methods and identify attributes most relevant and non-redundant for classification and function.
A unified taxonomy and nomenclature of cortical cell types
The paper proposes a formal, unified, transcriptome-based taxonomy, ontology, and nomenclature for cortical cell types that can be iteratively refined as data accumulate. Its usefulness depends on community adoption, supported by shared research tools enabling broader experimental access.
- Unified taxonomy: A data-driven transcriptomic classification should establish a formal, unified cell-type taxonomy, ontology, and nomenclature system generalizable across biological systems.The proposal follows lessons from genomics and treats classification principles as broadly applicable.
- Iterative refinement: The classification should be iteratively updated and refined as subsequent genomic and transcriptomic data accumulate, requiring a coherent nomenclature system.The passage compares this process with genome and transcriptome builds that changed substantially before becoming more stable.
- Community implementation: A cortex-, brain-, or body-wide nomenclature convention is essential as single-cell RNA-sequencing atlasing efforts expand, but the taxonomy will succeed only through community adoption.The proposal also calls for a community consortium to develop shared molecular and genetic tools, including standard antibody sets, to broaden experimental access.
A knowledge environment platform for community data aggregation
The paper proposes a community platform that uses a transcriptomics-based cell type taxonomy as an initial scaffold for aggregating broader biological data. A knowledge graph would provide a living, queryable, standardized, and peer-reviewed framework for an evolving cortical cell type ontology.
- Community platform: The proposed platform would use cell type classification and nomenclature as a community framework for accumulating data from the field.Possible implementations include an open, user-annotated website organized like a spreadsheet.
- Knowledge graph: A knowledge graph is proposed as the platform’s data structure, initially based on transcriptomic cell types and later incorporating information from many sources.The graph represents cell types as nodes and their statistical relations as links in a multidimensional space defined by cell type attributes.
- Knowledge graph: Community aggregation of raw data and metadata would support a flexible ontology that can integrate quantitative and qualitative cell type classifications.The framework is intended to remain actively derived and adaptable as new data are incorporated.
- Knowledge framework: The knowledge framework would be a living, updatable resource and dynamic database with query capability, accepting only peer-reviewed data in standardized formats and nomenclature.This design is intended to provide a common denominator for research in the field.
A community-based taxonomy of cortical cell types
The paper proposes a principled, transcriptome-based taxonomy of neocortical cell types organized as a hierarchical tree and defined by quantitative statistical criteria. It further recommends an open knowledge platform, unified cross-species nomenclature, and expert governance to support dissemination and revision.
- A community-based taxonomy of cortical cell types: The proposed taxonomy would define cortical cell clusters using quantitative statistical and probabilistic criteria derived initially from single-cell transcriptomic data.The classification is intended to be expressed as a hierarchical tree.
- A community-based taxonomy of cortical cell types: The taxonomy should be revised as other CAP datasets become available, allowing it to develop into a multimodal classification.The initial framework is molecularly driven but designed for modification as additional data emerge.
- A community-based taxonomy of cortical cell types: Community input should be organized through an open platform such as a knowledge graph to accelerate knowledge dissemination and avoid siloed data in a “publication graveyard.”The knowledge graph is presented as a way to aggregate knowledge across the field.
- A community-based taxonomy of cortical cell types: A unified nomenclature of cortical cell types valid across species is needed to anchor the taxonomy and knowledge graph.The proposal links standardized naming with the broader infrastructure for classification and knowledge aggregation.
- A community-based taxonomy of cortical cell types: An expert committee representing diverse approaches and disciplines could manage the classification, nomenclature, and knowledge graph.The committee would design the platform, statistical model, and rules for updating and revising the taxonomy.