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

BMC Bioinformatics

Research

Jeremy A. Miller¹  Rebecca Hodge¹  Trygve Bakken¹  Ed S. Lein¹  Lindsay Cowell²  Bali Pulendran³  Roger S. Lasken⁴  Ivan Chang⁴  Brian D. Aevermann⁴  Mark Novotny⁴  Nicholas J. Schork⁴  Yu Qian⁴  Alexandra Lee⁴  Jamison McCorrison⁴  Richard H. Scheuermann⁵ 

[1] Allen Institute for Brain Science, Seattle, 98103, Washington, USA;Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA;Department of Pathology and Laboratory Medicine, Emory University, 201 Dowman Dr, 30322, Atlanta, GA, USA;J. Craig Venter Institute, 4120 Capricorn Lane, 92037, La Jolla, CA, USA;J. Craig Venter Institute, 4120 Capricorn Lane, 92037, La Jolla, CA, USA;Department of Pathology, University of California San Diego, 9500 Gilman Drive, 92093, La Jolla, CA, USA;

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

DOI  :  10.1186/s12859-017-1977-1

来源: Springer

【 摘 要 】

BackgroundA 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.ResultsIn 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.ConclusionThe 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.

【 授权许可】

CC BY   
© The Author(s). 2017