The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 20; no. 3; pp. 793 - 809
• Main Authors: Gillespie, Thomas H., Tripathy, Shreejoy J., Sy, Mohameth François, Martone, Maryann E., Hill, Sean L.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2022; Springer Nature B.V
• Subjects: Bioinformatics; Biomedical and Life Sciences; Biomedicine; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Humans; Interneurons; Interoperability; Knowledge; Nervous system; Neurology; Neurons; Neurosciences; Ontology; Original; Original Article; Parvalbumin; Parvalbumins; Phenotype; Phenotypes; Ontology; Neurons; Cell types; Knowledge integration; Knowledge base; FAIR principles; Interoperability
• ISSN: 1539-2791; 1559-0089; 1559-0089
• DOI: 10.1007/s12021-022-09566-7

The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to many evidence-based types that are proposed based on the results of new techniques. Further, comparing different types across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO), that provides a standardized and automatable approach for naming cell types and normalizing their constituent phenotypes using identifiers from community ontologies as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological, and synaptic properties. Competency queries to this knowledge base demonstrate that the NPO knowledge model enables interoperability between the three test cases and neuron names commonly used in the literature.