Cell type discovery and representation in the era of high-content single cell phenotyping

Trygve Bakken, Lindsay Cowell, Brian D. Aevermann, Mark Novotny, Rebecca Hodge, Jeremy A. Miller, Alexandra Lee, Ivan Chang, Jamison McCorrison, Bali Pulendran, Yu Qian, Nicholas J. Schork, Roger S. Lasken, Ed S. Lein, Richard H. Scheuermann

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Background: A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. Results: In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including "context annotations" in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations. Conclusion: The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.

Original languageEnglish (US)
Article number559
JournalBMC Bioinformatics
Volume18
DOIs
StatePublished - Dec 21 2017

Keywords

  • Cell ontology
  • Cell phenotype
  • Cytometry
  • Marker genes
  • Neuron
  • Next generation sequencing
  • Open biomedical ontologies
  • Peripheral blood mononuclear cells
  • Single cell transcriptomics

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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    Bakken, T., Cowell, L., Aevermann, B. D., Novotny, M., Hodge, R., Miller, J. A., Lee, A., Chang, I., McCorrison, J., Pulendran, B., Qian, Y., Schork, N. J., Lasken, R. S., Lein, E. S., & Scheuermann, R. H. (2017). Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics, 18, [559]. https://doi.org/10.1186/s12859-017-1977-1