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

Area of Science:

• Neuroscience
• Genomics
• Cell Biology

Background:

• Understanding cortical circuits requires cataloging cellular diversity.
• Previous classification attempts using anatomical, physiological, or molecular features were limited by data.
• Single-cell transcriptomics provides high-throughput, comprehensive cell measurements.

Purpose of the Study:

• To propose a transcriptome-based taxonomy for mammalian neocortical cell types.
• To establish a unified, hierarchical classification system for brain cells.
• To create a foundation for studying cortical circuits.

Main Methods:

• Utilizing single-cell transcriptomics for systematic, high-throughput cell measurements.
• Applying statistical analyses to identify cell clusters based on gene expression.
• Integrating data from various approaches, developmental stages, and species.

Main Results:

• Transcriptomic data reveal cell clusters corresponding to known cell types.
• These cell types appear conserved across different cortical areas and species.
• The data support a systematic, data-driven approach to cell classification.

Conclusions:

• Adopting a transcriptome-based taxonomy is crucial for understanding cortical circuits.
• A hierarchical classification with standardized nomenclature, based on probabilistic definitions, is proposed.
• A community-based model, like a knowledge graph, can facilitate data aggregation and classification for broader biological applications.