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

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.

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

Fig. 1

Identification of myeloid cell subtypes using manual gating and directed automated filtering. A gating hierarchy (a series of iterative two-dimensional manual data partitions) has been established by the investigative team in which peripheral blood mononuclear cells (PBMC) are assessed for expression of HLA-DR and CD3, CD3- cells (Population #5) are assessed for expression of CD19 and CD14, CD19- cells (Population #7) are then assessed for expression of HLA-DR and CD16, HLA-DR+ cells (Population #10) are assessed for expression of HLA-DR and CD14, CD14- cells (Population #19) are assessed for expression of CD123 and CD141, CD141- cells (Population #21) are assessed for expression of CD11c and CD123, and CD11c + cells (Population #23) are assessed for expression of CD1c and CD16. Manual gating results are shown in the top panel; directed automated filter results using the DAFi method, a modified version of the FLOCK algorithm [21] are shown in the bottom panel

Fig. 2

Cell type representations in the Cell Ontology. a The expanded is_a hierarchy of the monocyte branch. b The expanded is_a hierarchy of the dendritic cell branch. c An example of a cell type term record for dendritic cell. Note the presence of both textual definitions in the “definition” field, and the components of the logical axioms in the “has part”, “lacks_plasma_membrane_part”, and “subClassOf” fields

Fig. 3

Cell type clustering and marker gene expression from RNA sequencing of single nuclei isolated from layer 1 cortex of post-mortem human brain. a Heatmap of CPM expression levels of a subset of genes that show selective expression in the 11 clusters of cells identified by principle component analysis (not show). An example of the statistical methods used to identify cell clusters and marker genes from single cell/single nuclei data can be found in [13]. b Violin plots of selected marker genes in each of the 11 cell clusters. c The expanded is_a hierarchy of the neuron branch of the Cell Ontology, with the interneuron sub-branch highlighted

Fig. 4

Proposed cell type names and definitions for cell types identified from the snRNAseq experiment shown in Fig. 3